Maintained by Difan Deng and Marius Lindauer.
The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.
Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.
Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.
2024
3198.
Xue, Yu; Han, Xiaolong; Neri, Ferrante; Qin, Jiafeng; Pelusi, Danilo
A Gradient-Guided Evolutionary Neural Architecture Search Journal Article
In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13, 2024.
@article{10465622,
title = {A Gradient-Guided Evolutionary Neural Architecture Search},
author = {Yu Xue and Xiaolong Han and Ferrante Neri and Jiafeng Qin and Danilo Pelusi},
url = {https://ieeexplore.ieee.org/abstract/document/10465622},
doi = {10.1109/TNNLS.2024.3371432},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {1-13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3197.
Gao, Yuan; ZHANG, WEIZHONG; Luo, Wenhan; Ma, Lin; Yu, Jin-Gang; Xia, Gui-Song; Ma, Jiayi
Free Lunches in Auxiliary Learning: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost Proceedings Article
In: The Twelfth International Conference on Learning Representations, 2024.
@inproceedings{<LineBreak>gao2024free,
title = {Free Lunches in Auxiliary Learning: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost},
author = {Yuan Gao and WEIZHONG ZHANG and Wenhan Luo and Lin Ma and Jin-Gang Yu and Gui-Song Xia and Jiayi Ma},
url = {https://openreview.net/forum?id=cINwAhrgLf},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {The Twelfth International Conference on Learning Representations},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
3196.
Schiessler, Elisabeth J.; Aydin, Roland C.; Cyron, Christian J.
ECToNAS: Evolutionary Cross-Topology Neural Architecture Search Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2403-05123,
title = {ECToNAS: Evolutionary Cross-Topology Neural Architecture Search},
author = {Elisabeth J. Schiessler and Roland C. Aydin and Christian J. Cyron},
url = {https://doi.org/10.48550/arXiv.2403.05123},
doi = {10.48550/ARXIV.2403.05123},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2403.05123},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3195.
Xie, Weisheng; Li, Hui; Fang, Xuwei; Li, Shaoyuan
DARTS-PT-CORE: Collaborative and Regularized Perturbation-based Architecture Selection for differentiable NAS Journal Article
In: Neurocomputing, vol. 580, pp. 127522, 2024, ISSN: 0925-2312.
@article{XIE2024127522b,
title = {DARTS-PT-CORE: Collaborative and Regularized Perturbation-based Architecture Selection for differentiable NAS},
author = {Weisheng Xie and Hui Li and Xuwei Fang and Shaoyuan Li},
url = {https://www.sciencedirect.com/science/article/pii/S0925231224002935},
doi = {https://doi.org/10.1016/j.neucom.2024.127522},
issn = {0925-2312},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Neurocomputing},
volume = {580},
pages = {127522},
abstract = {DARTS-PT is a well-known differentiable NAS method that measures the operation strength through its contribution to the supernet performance, extracting architecture from the underlying supernet. However, persistent issues of degraded architecture in DARTS-PT have been identified in recent studies. In response, we undertake a comprehensive analysis of this performance degradation issue and identify two primary contributing factors: the unfavorable competition among correlated operations during the operation selection process and the unfair advantage of parameter-free operations within DARTS-PT supernet. Building upon these findings, we propose DARTS-PT-CORE, a novel architecture selection algorithm that incorporates a collaborative operation competition mechanism and a regularization technique in the perturbation-based architecture selection approach. Our method aims to mitigate the negative effects of competition among correlated operations, yielding more reliable operation contribution scores. Furthermore, our regularization technique addresses the unfair advantage of parameter-free operations, facilitating a more balanced architecture selection process. Extensive experiments conducted on various datasets and search spaces indicate that DARTS-PT-CORE outperforms other state of-the-art methods. Specifically, in the DARTS search space, DARTS-PT-CORE achieves 2.43% test error on CIFAR10 and 16.23% test error on CIFAR100, while the search time is less than 0.8 GPU days. When transferring to ImageNet, DARTS-PT-CORE achieves 24.97% top-1 error. Such results underscore the effectiveness of our method in enhancing the reliability and balance of architecture selection in differentiable NAS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
DARTS-PT is a well-known differentiable NAS method that measures the operation strength through its contribution to the supernet performance, extracting architecture from the underlying supernet. However, persistent issues of degraded architecture in DARTS-PT have been identified in recent studies. In response, we undertake a comprehensive analysis of this performance degradation issue and identify two primary contributing factors: the unfavorable competition among correlated operations during the operation selection process and the unfair advantage of parameter-free operations within DARTS-PT supernet. Building upon these findings, we propose DARTS-PT-CORE, a novel architecture selection algorithm that incorporates a collaborative operation competition mechanism and a regularization technique in the perturbation-based architecture selection approach. Our method aims to mitigate the negative effects of competition among correlated operations, yielding more reliable operation contribution scores. Furthermore, our regularization technique addresses the unfair advantage of parameter-free operations, facilitating a more balanced architecture selection process. Extensive experiments conducted on various datasets and search spaces indicate that DARTS-PT-CORE outperforms other state of-the-art methods. Specifically, in the DARTS search space, DARTS-PT-CORE achieves 2.43% test error on CIFAR10 and 16.23% test error on CIFAR100, while the search time is less than 0.8 GPU days. When transferring to ImageNet, DARTS-PT-CORE achieves 24.97% top-1 error. Such results underscore the effectiveness of our method in enhancing the reliability and balance of architecture selection in differentiable NAS.
3194.
Cui, Suhan; Mitra, Prasenjit
Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2403-04086,
title = {Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records},
author = {Suhan Cui and Prasenjit Mitra},
url = {https://doi.org/10.48550/arXiv.2403.04086},
doi = {10.48550/ARXIV.2403.04086},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2403.04086},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3193.
Jin, 2 Jinjie Huang Cong
Enhanced Differentiable Architecture Search Based on Asymptotic Regularization Journal Article
In: Computers, Materials & Continua, vol. 78, no. 2, pp. 1547–1568, 2024, ISSN: 1546-2226.
@article{cmc.2023.047489,
title = {Enhanced Differentiable Architecture Search Based on Asymptotic Regularization},
author = {2 Jinjie Huang Cong Jin},
url = {http://www.techscience.com/cmc/v78n2/55579},
doi = {10.32604/cmc.2023.047489},
issn = {1546-2226},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers, Materials & Continua},
volume = {78},
number = {2},
pages = {1547–1568},
abstract = {In differentiable search architecture search methods, a more efficient search space design can significantly improve the performance of the searched architecture, thus requiring people to carefully define the search space with different complexity according to various operations. Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search. With this in mind, we propose a faster and more efficient differentiable architecture search method, AllegroNAS. Firstly, we introduce a more efficient search space enriched by the introduction of two redefined convolution modules. Secondly, we utilize a more efficient architectural parameter regularization method, mitigating the overfitting problem during the search process and reducing the error brought about by gradient approximation. Meanwhile, we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search procedure. Moreover, group convolution and data augmentation are employed to reduce the computational cost. Finally, through extensive experiments on several public datasets, we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space, thus validating the effectiveness of our approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In differentiable search architecture search methods, a more efficient search space design can significantly improve the performance of the searched architecture, thus requiring people to carefully define the search space with different complexity according to various operations. Meanwhile rationalizing the search strategies to explore the well-defined search space will further improve the speed and efficiency of architecture search. With this in mind, we propose a faster and more efficient differentiable architecture search method, AllegroNAS. Firstly, we introduce a more efficient search space enriched by the introduction of two redefined convolution modules. Secondly, we utilize a more efficient architectural parameter regularization method, mitigating the overfitting problem during the search process and reducing the error brought about by gradient approximation. Meanwhile, we introduce a natural exponential cosine annealing method to make the learning rate of the neural network training process more suitable for the search procedure. Moreover, group convolution and data augmentation are employed to reduce the computational cost. Finally, through extensive experiments on several public datasets, we demonstrate that our method can more swiftly search for better-performing neural network architectures in a more efficient search space, thus validating the effectiveness of our approach.
3192.
Chen, Jialin; Cai, Zhiqiang; Xu, Ke; Wu, Di; Cao, Wei
Qubit-Wise Architecture Search Method for Variational Quantum Circuits Technical Report
2024.
@techreport{chen2024qubitwise,
title = {Qubit-Wise Architecture Search Method for Variational Quantum Circuits},
author = {Jialin Chen and Zhiqiang Cai and Ke Xu and Di Wu and Wei Cao},
url = {https://arxiv.org/abs/2403.04268},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3191.
Wang, Wenna; Ran, Lingyan; Yin, Hanlin; Sun, Mingjun; Zhang, Xiuwei; Zhang, Yanning
Hierarchical Shared Architecture Search for Real-Time Semantic Segmentation of Remote Sensing Images Journal Article
In: IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-13, 2024.
@article{10460490,
title = {Hierarchical Shared Architecture Search for Real-Time Semantic Segmentation of Remote Sensing Images},
author = {Wenna Wang and Lingyan Ran and Hanlin Yin and Mingjun Sun and Xiuwei Zhang and Yanning Zhang},
url = {https://ieeexplore.ieee.org/abstract/document/10460490},
doi = {10.1109/TGRS.2024.3373493},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {62},
pages = {1-13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3190.
Ji, Mengfei; Chang, Yuchun; Zhang, Baolin; Al-Ars, Zaid
NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2403-01845,
title = {NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models},
author = {Mengfei Ji and Yuchun Chang and Baolin Zhang and Zaid Al-Ars},
url = {https://doi.org/10.48550/arXiv.2403.01845},
doi = {10.48550/ARXIV.2403.01845},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2403.01845},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3189.
Zhu, Wenbo; Hu, Yongcong; Zhu, Zhengjun; Yeh, Wei-Chang; Li, Haibing; Zhang, Zhongbo; Fu, Weijie
Searching by Topological Complexity: Lightweight Neural Architecture Search for Coal and Gangue Classification Journal Article
In: Mathematics, vol. 12, no. 5, 2024, ISSN: 2227-7390.
@article{math12050759,
title = {Searching by Topological Complexity: Lightweight Neural Architecture Search for Coal and Gangue Classification},
author = {Wenbo Zhu and Yongcong Hu and Zhengjun Zhu and Wei-Chang Yeh and Haibing Li and Zhongbo Zhang and Weijie Fu},
url = {https://www.mdpi.com/2227-7390/12/5/759},
doi = {10.3390/math12050759},
issn = {2227-7390},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Mathematics},
volume = {12},
number = {5},
abstract = {Lightweight and adaptive adjustment are key research directions for deep neural networks (DNNs). In coal industry mining, frequent changes in raw coal sources and production batches can cause uneven distribution of appearance features, leading to concept drift problems. The network architecture and parameters should be adjusted frequently to avoid a decline in model accuracy. This poses a significant challenge for those without specialist expertise. Although the Neural Architecture Search (NAS) has a strong ability to automatically generate networks, enabling the automatic design of highly accurate networks, it often comes with complex internal topological connections. These redundant architectures do not always effectively improve network performance, especially in resource-constrained environments, where their computational efficiency is significantly reduced. In this paper, we propose a method called Topology Complexity Neural Architecture Search (TCNAS). TCNAS proposes a new method for evaluating the topological complexity of neural networks and uses both topological complexity and accuracy to guide the search, effectively obtaining lightweight and efficient networks. TCNAS employs an adaptive shrinking search space optimization method, which gradually eliminates poorly performing cells to reduce the search space, thereby improving search efficiency and solving the problem of space explosion. In the classification experiments of coal and gangue, the optimal network designed by TCNAS has an accuracy of 83.3%. And its structure is much simpler, which is about 1/53 of the parameters of the network dedicated to coal and gangue recognition. Experiments have shown that TCNAS is able to generate networks that are both efficient and simple for resource-constrained industrial applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lightweight and adaptive adjustment are key research directions for deep neural networks (DNNs). In coal industry mining, frequent changes in raw coal sources and production batches can cause uneven distribution of appearance features, leading to concept drift problems. The network architecture and parameters should be adjusted frequently to avoid a decline in model accuracy. This poses a significant challenge for those without specialist expertise. Although the Neural Architecture Search (NAS) has a strong ability to automatically generate networks, enabling the automatic design of highly accurate networks, it often comes with complex internal topological connections. These redundant architectures do not always effectively improve network performance, especially in resource-constrained environments, where their computational efficiency is significantly reduced. In this paper, we propose a method called Topology Complexity Neural Architecture Search (TCNAS). TCNAS proposes a new method for evaluating the topological complexity of neural networks and uses both topological complexity and accuracy to guide the search, effectively obtaining lightweight and efficient networks. TCNAS employs an adaptive shrinking search space optimization method, which gradually eliminates poorly performing cells to reduce the search space, thereby improving search efficiency and solving the problem of space explosion. In the classification experiments of coal and gangue, the optimal network designed by TCNAS has an accuracy of 83.3%. And its structure is much simpler, which is about 1/53 of the parameters of the network dedicated to coal and gangue recognition. Experiments have shown that TCNAS is able to generate networks that are both efficient and simple for resource-constrained industrial applications.
3188.
Zou, Juan; Jiang, Weiwei; Xia, Yizhang; Liu, Yuan; Hou, Zhanglu
G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth Bachelor Thesis
2024.
@bachelorthesis{DBLP:journals/corr/abs-2403-02667,
title = {G-EvoNAS: Evolutionary Neural Architecture Search Based on Network Growth},
author = {Juan Zou and Weiwei Jiang and Yizhang Xia and Yuan Liu and Zhanglu Hou},
url = {https://doi.org/10.48550/arXiv.2403.02667},
doi = {10.48550/ARXIV.2403.02667},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2403.02667},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
3187.
Akhauri, Yash; Abdelfattah, Mohamed S.
On Latency Predictors for Neural Architecture Search Bachelor Thesis
2024.
@bachelorthesis{DBLP:journals/corr/abs-2403-02446,
title = {On Latency Predictors for Neural Architecture Search},
author = {Yash Akhauri and Mohamed S. Abdelfattah},
url = {https://doi.org/10.48550/arXiv.2403.02446},
doi = {10.48550/ARXIV.2403.02446},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2403.02446},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
3186.
Xue, Wenyuan; Lu, Yichen; Wang, Zhi; Cao, Shengxian; Sui, Mengxuan; Yang, Yuan; Li, Jiyuan; Xie, Yubin
Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization Journal Article
In: Energy, vol. 294, pp. 130860, 2024, ISSN: 0360-5442.
@article{XUE2024130860,
title = {Reconstructing near-water-wall temperature in coal-fired boilers using improved transfer learning and hidden layer configuration optimization},
author = {Wenyuan Xue and Yichen Lu and Zhi Wang and Shengxian Cao and Mengxuan Sui and Yuan Yang and Jiyuan Li and Yubin Xie},
url = {https://www.sciencedirect.com/science/article/pii/S0360544224006327},
doi = {https://doi.org/10.1016/j.energy.2024.130860},
issn = {0360-5442},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Energy},
volume = {294},
pages = {130860},
abstract = {The temperature field is a critical factor for ensuring the safe combustion and energy conservation in boilers. However, an effective method for reconstructing the temperature field near the water wall is still under exploration. In this paper, a method for online reconstruction of the temperature field distribution near the water wall in a 330 MW tangentially fired coal boiler is proposed, which progresses from a well-established model for the entire furnace. A two-branch fusion network for transfer learning (TBFN-TL) method is proposed, incorporating additional key parameters, heat flux, during the transfer process to enhance the effectiveness. A Bayesian hierarchical neural architecture search (BHNAS) method is proposed to optimize the configuration of the hidden layers in building neural networks. Compared with computational fluid dynamics (CFD) results, the mean absolute percentage error (MAPE) of the reconstruction results for the entire furnace model, traditional transfer learning methods, and the proposed TBFN-TL are 11.57%, 5.738%, and 2.052%, respectively, demonstrating a significant enhancement. The proposed BHNAS method extends the search optimization space, obtaining more excellent configurations for the hidden layer nodes. The proposed methods have significant implications for temperature field reconstruction, the field of transfer learning, and the optimization of hidden layer configurations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The temperature field is a critical factor for ensuring the safe combustion and energy conservation in boilers. However, an effective method for reconstructing the temperature field near the water wall is still under exploration. In this paper, a method for online reconstruction of the temperature field distribution near the water wall in a 330 MW tangentially fired coal boiler is proposed, which progresses from a well-established model for the entire furnace. A two-branch fusion network for transfer learning (TBFN-TL) method is proposed, incorporating additional key parameters, heat flux, during the transfer process to enhance the effectiveness. A Bayesian hierarchical neural architecture search (BHNAS) method is proposed to optimize the configuration of the hidden layers in building neural networks. Compared with computational fluid dynamics (CFD) results, the mean absolute percentage error (MAPE) of the reconstruction results for the entire furnace model, traditional transfer learning methods, and the proposed TBFN-TL are 11.57%, 5.738%, and 2.052%, respectively, demonstrating a significant enhancement. The proposed BHNAS method extends the search optimization space, obtaining more excellent configurations for the hidden layer nodes. The proposed methods have significant implications for temperature field reconstruction, the field of transfer learning, and the optimization of hidden layer configurations.
3185.
Xue, Yu; Chen, Kun; Neri, Ferrante
Differentiable Architecture Search with Attention Mechanisms for Generative Adversarial Networks Journal Article
In: IEEE transactions on emerging topics in computational intelligence, 2024, ISSN: 2471-285X.
@article{NeriFerrante2024DASw,
title = {Differentiable Architecture Search with Attention Mechanisms for Generative Adversarial Networks},
author = {Yu Xue and Kun Chen and Ferrante Neri},
url = {https://openresearch.surrey.ac.uk/esploro/outputs/journalArticle/Differentiable-Architecture-Search-with-Attention-Mechanisms/99860066602346?institution=44SUR_INST},
issn = {2471-285X},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE transactions on emerging topics in computational intelligence},
publisher = {IEEE},
abstract = {—Generative adversarial networks (GANs) are machine learning algorithms that can efficiently generate data such as images. Although GANs are very popular, their training usually lacks stability, with the generator and discriminator networks failing to converge during the training process. To address this problem and improve the stability of GANs, in this paper, we automate the design of stable GANs architectures through a novel approach: differentiable architecture search with attention mechanisms for generative adversarial networks (DAMGAN). We construct a generator supernet and search for the optimal generator network within it. We propose incorporating two attention mechanisms between each pair of nodes in the supernet. The first attention mechanism, down attention, selects the optimal candidate operation of each edge in the supernet, while the second attention mechanism, up attention, improves the training stability of the supernet and limits the computational cost of the search by selecting the most important feature maps for the following candidate operations. Experimental results show that the architectures searched by our method obtain a state-of-the-art inception score (IS) of 8.99 and a very competitive Fréchet inception distance (FID) of 10.27 on the CIFAR-10 dataset. Competitive results were also obtained on the STL-10 dataset (IS = 10.35},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
—Generative adversarial networks (GANs) are machine learning algorithms that can efficiently generate data such as images. Although GANs are very popular, their training usually lacks stability, with the generator and discriminator networks failing to converge during the training process. To address this problem and improve the stability of GANs, in this paper, we automate the design of stable GANs architectures through a novel approach: differentiable architecture search with attention mechanisms for generative adversarial networks (DAMGAN). We construct a generator supernet and search for the optimal generator network within it. We propose incorporating two attention mechanisms between each pair of nodes in the supernet. The first attention mechanism, down attention, selects the optimal candidate operation of each edge in the supernet, while the second attention mechanism, up attention, improves the training stability of the supernet and limits the computational cost of the search by selecting the most important feature maps for the following candidate operations. Experimental results show that the architectures searched by our method obtain a state-of-the-art inception score (IS) of 8.99 and a very competitive Fréchet inception distance (FID) of 10.27 on the CIFAR-10 dataset. Competitive results were also obtained on the STL-10 dataset (IS = 10.35
3184.
Shang, Ronghua; Liu, Hangcheng; Li, Wenzheng; Zhang, Weitong; Ma, Teng; Jiao, Licheng
An efficient evolutionary architecture search for variational autoencoder with alternating optimization and adaptive crossover Journal Article
In: Swarm and Evolutionary Computation, vol. 86, pp. 101520, 2024, ISSN: 2210-6502.
@article{SHANG2024101520,
title = {An efficient evolutionary architecture search for variational autoencoder with alternating optimization and adaptive crossover},
author = {Ronghua Shang and Hangcheng Liu and Wenzheng Li and Weitong Zhang and Teng Ma and Licheng Jiao},
url = {https://www.sciencedirect.com/science/article/pii/S2210650224000531},
doi = {https://doi.org/10.1016/j.swevo.2024.101520},
issn = {2210-6502},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Swarm and Evolutionary Computation},
volume = {86},
pages = {101520},
abstract = {Variational autoencoder is a commonly unsupervised learning model. However, its complex structure hinders the utilization of the network architecture search algorithm to release researchers from tedious manual design. To design excellent architectures automatically, this paper proposes an efficient evolutionary architecture search for variational autoencoder with alternating optimization and adaptive crossover(AOC-VAE). Firstly, to alleviate the problem of large search space when automatically designing variational autoencoders, AOC-VAE designs an alternating optimized search mechanism based on the specific coupling of encoder and decoder in variational autoencoders, which reduces the original huge search space almost to half. Then, AOC-VAE can find quickly the optimal individual in the solution space by designing an adaptive crossover mechanism. In early evolutionary period, the structural differences between individuals are relatively significant, making crossover operations more inclined to exchange structural information between individuals. As evolution progresses, the individual structures in the population tend to be similar, and the exchange of information concentrates on the parameter. Finally, in the optimization process, a fitness evaluation mechanism based on dynamic weights is designed to accurately find out the outstanding individuals under the current optimization goal. Individual fitness in the population is more inclined to be affected by the current optimization goal, thus guiding the population to evolve according to the optimization goal at different stages. AOC-VAE is verified on MNIST, SVHN, CIFAR-10, and CIFAR-100 benchmark datasets and compared with 14 algorithms. The experimental results show that the VAE network structure designed by the AOC-VAE performs well in the image classification task.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Variational autoencoder is a commonly unsupervised learning model. However, its complex structure hinders the utilization of the network architecture search algorithm to release researchers from tedious manual design. To design excellent architectures automatically, this paper proposes an efficient evolutionary architecture search for variational autoencoder with alternating optimization and adaptive crossover(AOC-VAE). Firstly, to alleviate the problem of large search space when automatically designing variational autoencoders, AOC-VAE designs an alternating optimized search mechanism based on the specific coupling of encoder and decoder in variational autoencoders, which reduces the original huge search space almost to half. Then, AOC-VAE can find quickly the optimal individual in the solution space by designing an adaptive crossover mechanism. In early evolutionary period, the structural differences between individuals are relatively significant, making crossover operations more inclined to exchange structural information between individuals. As evolution progresses, the individual structures in the population tend to be similar, and the exchange of information concentrates on the parameter. Finally, in the optimization process, a fitness evaluation mechanism based on dynamic weights is designed to accurately find out the outstanding individuals under the current optimization goal. Individual fitness in the population is more inclined to be affected by the current optimization goal, thus guiding the population to evolve according to the optimization goal at different stages. AOC-VAE is verified on MNIST, SVHN, CIFAR-10, and CIFAR-100 benchmark datasets and compared with 14 algorithms. The experimental results show that the VAE network structure designed by the AOC-VAE performs well in the image classification task.
3183.
Rahman, Md Hafizur; Chakraborty, Prabuddha
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-18443b,
title = {LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs},
author = {Md Hafizur Rahman and Prabuddha Chakraborty},
url = {https://doi.org/10.48550/arXiv.2402.18443},
doi = {10.48550/ARXIV.2402.18443},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.18443},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3182.
Garavagno, Andrea Mattia; Ragusa, Edoardo; Frisoli, Antonio; Gastaldo, Paolo
Running hardware-aware neural architecture search on embedded devices under 512MB of RAM Proceedings Article
In: 2024 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-2, 2024.
@inproceedings{10444268,
title = {Running hardware-aware neural architecture search on embedded devices under 512MB of RAM},
author = {Andrea Mattia Garavagno and Edoardo Ragusa and Antonio Frisoli and Paolo Gastaldo},
url = {https://ieeexplore.ieee.org/abstract/document/10444268},
doi = {10.1109/ICCE59016.2024.10444268},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 IEEE International Conference on Consumer Electronics (ICCE)},
pages = {1-2},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
3181.
Gambella, Matteo; Pittorino, Fabrizio; Roveri, Manuel
FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-19102,
title = {FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness},
author = {Matteo Gambella and Fabrizio Pittorino and Manuel Roveri},
url = {https://doi.org/10.48550/arXiv.2402.19102},
doi = {10.48550/ARXIV.2402.19102},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.19102},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3180.
Wu, Xiang; Zhang, Yong-Ting; Lai, Khin-Wee; Yang, Ming-Zhao; Yang, Ge-Lan; Wang, Huan-Huan
A Novel Centralized Federated Deep Fuzzy Neural Network with Multi-objectives Neural Architecture Search for Epistatic Detection Journal Article
In: IEEE Transactions on Fuzzy Systems, pp. 1-13, 2024.
@article{10445010,
title = {A Novel Centralized Federated Deep Fuzzy Neural Network with Multi-objectives Neural Architecture Search for Epistatic Detection},
author = {Xiang Wu and Yong-Ting Zhang and Khin-Wee Lai and Ming-Zhao Yang and Ge-Lan Yang and Huan-Huan Wang},
url = {https://ieeexplore.ieee.org/abstract/document/10445010},
doi = {10.1109/TFUZZ.2024.3369944},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Fuzzy Systems},
pages = {1-13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3179.
Gomez-Rosero, Santiago; Capretz, Miriam A. M.
Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions Journal Article
In: Applied Soft Computing, vol. 155, pp. 111442, 2024, ISSN: 1568-4946.
@article{GOMEZROSERO2024111442,
title = {Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions},
author = {Santiago Gomez-Rosero and Miriam A. M. Capretz},
url = {https://www.sciencedirect.com/science/article/pii/S1568494624002163},
doi = {https://doi.org/10.1016/j.asoc.2024.111442},
issn = {1568-4946},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Applied Soft Computing},
volume = {155},
pages = {111442},
abstract = {Deep neural networks have become the benchmark in diverse fields such as energy consumption forecasting, speech recognition, and anomaly detection, owing to their ability to efficiently process and analyse data. However, they face challenges in managing the complexity and variability in time series data, often leading to increased model complexity and prolonged search duration during parameter tuning. This paper proposes a novel anomaly detection approach through evolutionary neural architecture search (AD-ENAS), which is specifically designed for time series data. The proposed approach focuses on the search for the optimal and minimal neural network architecture. The AD-ENAS method consists of two main phases: architecture evolution and weight adjustment. The architecture evolution phase highlights the importance of neural network architecture by evaluating the fitness of each network agent using shared weight values. Subsequently, the convolutional matrix adaptation technique is used in the next phase for optimal weight adjustment of the neural network. The proposed AD-ENAS method operates without relying on differentiable functions, thus expanding the scope of neural network design beyond traditional backpropagation-based approaches. Various non-differentiable loss functions are explored to facilitate effective architecture search and weight adjustment. Comparative experiments are conducted with five baseline anomaly detection methods on three well-known datasets from reputable sources such as NASA SMAP, NASA MSL and Yahoo S5-A1. The results demonstrate that the AD-ENAS approach effectively evolves neural network architectures, outperforming baseline methods with F1 scores across the three datasets (MSL: 0.942, SMAP: 0.961, Yahoo S5-A1: 0.988) with non-differentiable loss functions, showcasing its efficacy in detecting anomalies in time series data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Deep neural networks have become the benchmark in diverse fields such as energy consumption forecasting, speech recognition, and anomaly detection, owing to their ability to efficiently process and analyse data. However, they face challenges in managing the complexity and variability in time series data, often leading to increased model complexity and prolonged search duration during parameter tuning. This paper proposes a novel anomaly detection approach through evolutionary neural architecture search (AD-ENAS), which is specifically designed for time series data. The proposed approach focuses on the search for the optimal and minimal neural network architecture. The AD-ENAS method consists of two main phases: architecture evolution and weight adjustment. The architecture evolution phase highlights the importance of neural network architecture by evaluating the fitness of each network agent using shared weight values. Subsequently, the convolutional matrix adaptation technique is used in the next phase for optimal weight adjustment of the neural network. The proposed AD-ENAS method operates without relying on differentiable functions, thus expanding the scope of neural network design beyond traditional backpropagation-based approaches. Various non-differentiable loss functions are explored to facilitate effective architecture search and weight adjustment. Comparative experiments are conducted with five baseline anomaly detection methods on three well-known datasets from reputable sources such as NASA SMAP, NASA MSL and Yahoo S5-A1. The results demonstrate that the AD-ENAS approach effectively evolves neural network architectures, outperforming baseline methods with F1 scores across the three datasets (MSL: 0.942, SMAP: 0.961, Yahoo S5-A1: 0.988) with non-differentiable loss functions, showcasing its efficacy in detecting anomalies in time series data.
3178.
Rahman, Md Hafizur; Chakraborty, Prabuddha
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-18443,
title = {LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs},
author = {Md Hafizur Rahman and Prabuddha Chakraborty},
url = {https://doi.org/10.48550/arXiv.2402.18443},
doi = {10.48550/ARXIV.2402.18443},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.18443},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3177.
Zhang, Pengyu; Zhou, Yingbo; Hu, Ming; Feng, Junxian; Weng, Jiawen; Chen, Mingsong
Personalized Federated Instruction Tuning via Neural Architecture Search Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-16919,
title = {Personalized Federated Instruction Tuning via Neural Architecture Search},
author = {Pengyu Zhang and Yingbo Zhou and Ming Hu and Junxian Feng and Jiawen Weng and Mingsong Chen},
url = {https://doi.org/10.48550/arXiv.2402.16919},
doi = {10.48550/ARXIV.2402.16919},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.16919},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3176.
Sukthanker, Rhea Sanjay; Zela, Arber; Staffler, Benedikt; Dooley, Samuel; Grabocka, Josif; Hutter, Frank
Multi-objective Differentiable Neural Architecture Search Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-18213,
title = {Multi-objective Differentiable Neural Architecture Search},
author = {Rhea Sanjay Sukthanker and Arber Zela and Benedikt Staffler and Samuel Dooley and Josif Grabocka and Frank Hutter},
url = {https://doi.org/10.48550/arXiv.2402.18213},
doi = {10.48550/ARXIV.2402.18213},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.18213},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3175.
Gao, Peng; Liu, Xiao; Wang, Yu; Yuan, Ru-Yue
Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-16570,
title = {Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking},
author = {Peng Gao and Xiao Liu and Yu Wang and Ru-Yue Yuan},
url = {https://doi.org/10.48550/arXiv.2402.16570},
doi = {10.48550/ARXIV.2402.16570},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.16570},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3174.
Zhang, Rui; Zhang, Peng-Yun; Gao, Mei-Rong; Ma, Jian-Zhe; Pan, Li-Hu
Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement Journal Article
In: Applied Soft Computing, vol. 155, pp. 111440, 2024, ISSN: 1568-4946.
@article{ZHANG2024111440,
title = {Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement},
author = {Rui Zhang and Peng-Yun Zhang and Mei-Rong Gao and Jian-Zhe Ma and Li-Hu Pan},
url = {https://www.sciencedirect.com/science/article/pii/S156849462400214X},
doi = {https://doi.org/10.1016/j.asoc.2024.111440},
issn = {1568-4946},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Applied Soft Computing},
volume = {155},
pages = {111440},
abstract = {The time and effort required to manually design deep neural architectures is extremely high, which has led to the development of neural architecture search technology as an automatic architecture design method. However, the neural architecture search convergence process is slow and expensive, and the process requires training a large number of candidate architectures to get the final result. If the final accuracy of an architecture can be predicted from its initial state, this problem can be greatly alleviated. Therefore, this paper proposes a low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement, which takes 1) the difference matrix value between the feature map generated in the untrained architecture and the original image, and 2) the predicted accuracy of the neural network as evaluation indices. A new multi-index weight comprehensive measurement strategy was introduced to comprehensively score the multi-index, the real architecture performance can be approximately represented by score, which greatly reduces the cost of architecture evaluation. The experimental show that the proposed scoring strategy is highly correlated with real architecture accuracy. In the practical engineering application research, this strategy can search a high-performance architecture with an accuracy of 96.2% within 343.3 s, which proves that the proposed strategy can significantly improve the search efficiency in practical applications, reduce the subjectivity of artificial architecture design, and promote the application of practical time-consuming projects.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The time and effort required to manually design deep neural architectures is extremely high, which has led to the development of neural architecture search technology as an automatic architecture design method. However, the neural architecture search convergence process is slow and expensive, and the process requires training a large number of candidate architectures to get the final result. If the final accuracy of an architecture can be predicted from its initial state, this problem can be greatly alleviated. Therefore, this paper proposes a low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement, which takes 1) the difference matrix value between the feature map generated in the untrained architecture and the original image, and 2) the predicted accuracy of the neural network as evaluation indices. A new multi-index weight comprehensive measurement strategy was introduced to comprehensively score the multi-index, the real architecture performance can be approximately represented by score, which greatly reduces the cost of architecture evaluation. The experimental show that the proposed scoring strategy is highly correlated with real architecture accuracy. In the practical engineering application research, this strategy can search a high-performance architecture with an accuracy of 96.2% within 343.3 s, which proves that the proposed strategy can significantly improve the search efficiency in practical applications, reduce the subjectivity of artificial architecture design, and promote the application of practical time-consuming projects.
3173.
Risso, Matteo; Daghero, Francesco; Motetti, Beatrice Alessandra; Pagliari, Daniele Jahier; Macii, Enrico; Poncino, Massimo; Burrello, Alessio
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-15273,
title = {Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones},
author = {Matteo Risso and Francesco Daghero and Beatrice Alessandra Motetti and Daniele Jahier Pagliari and Enrico Macii and Massimo Poncino and Alessio Burrello},
url = {https://doi.org/10.48550/arXiv.2402.15273},
doi = {10.48550/ARXIV.2402.15273},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.15273},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3172.
Garg, Manav S.
Automated Machine Learning: Evaluation without Training Journal Article
In: 2024.
@article{Garg_2024,
title = {Automated Machine Learning: Evaluation without Training},
author = {Manav S. Garg},
url = {http://dx.doi.org/10.36227/techrxiv.170595826.64565617/v1},
doi = {10.36227/techrxiv.170595826.64565617/v1},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3171.
Huang, Hongtao; Chang, Xiaojun; Hu, Wen; Yao, Lina
MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-13525,
title = {MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment},
author = {Hongtao Huang and Xiaojun Chang and Wen Hu and Lina Yao},
url = {https://doi.org/10.48550/arXiv.2402.13525},
doi = {10.48550/ARXIV.2402.13525},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.13525},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3170.
Cao, Tue Minh; Tran, Nhat Hong; Pham, Hieu H.; Nguyen, Hung Thanh; Nguyen, Le P.
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-13822,
title = {MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification},
author = {Tue Minh Cao and Nhat Hong Tran and Hieu H. Pham and Hung Thanh Nguyen and Le P. Nguyen},
url = {https://doi.org/10.48550/arXiv.2402.13822},
doi = {10.48550/ARXIV.2402.13822},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.13822},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3169.
Putra, Rachmad Vidya Wicaksana; Shafique, Muhammad
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network Systems Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-11322b,
title = {SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network Systems},
author = {Rachmad Vidya Wicaksana Putra and Muhammad Shafique},
url = {https://doi.org/10.48550/arXiv.2402.11322},
doi = {10.48550/ARXIV.2402.11322},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.11322},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3168.
Bouzidi, Halima; Niar, Sma"ıl; Ouarnoughi, Hamza; Talbi, El-Ghazali
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-13204,
title = {SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search},
author = {Halima Bouzidi and Sma"ıl Niar and Hamza Ouarnoughi and El-Ghazali Talbi},
url = {https://doi.org/10.48550/arXiv.2402.13204},
doi = {10.48550/ARXIV.2402.13204},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.13204},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3167.
Kundu, Souvik; Sarah, Anthony; Joshi, Vinay; Omer, Om Ji; Subramoney, Sreenivas
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-11780,
title = {CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware},
author = {Souvik Kundu and Anthony Sarah and Vinay Joshi and Om Ji Omer and Sreenivas Subramoney},
url = {https://doi.org/10.48550/arXiv.2402.11780},
doi = {10.48550/ARXIV.2402.11780},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.11780},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3166.
Putra, Rachmad Vidya Wicaksana; Shafique, Muhammad
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network Systems Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-11322,
title = {SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network Systems},
author = {Rachmad Vidya Wicaksana Putra and Muhammad Shafique},
url = {https://doi.org/10.48550/arXiv.2402.11322},
doi = {10.48550/ARXIV.2402.11322},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.11322},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3165.
Wang, Junjue; Zhong, Yanfei; Ma, Ailong; Zheng, Zhuo; Wan, Yuting; Zhang, Liangpei
LoveNAS: Towards multi-scene land-cover mapping via hierarchical searching adaptive network Journal Article
In: ISPRS Journal of Photogrammetry and Remote Sensing, vol. 209, pp. 265-278, 2024, ISSN: 0924-2716.
@article{WANG2024265,
title = {LoveNAS: Towards multi-scene land-cover mapping via hierarchical searching adaptive network},
author = {Junjue Wang and Yanfei Zhong and Ailong Ma and Zhuo Zheng and Yuting Wan and Liangpei Zhang},
url = {https://www.sciencedirect.com/science/article/pii/S0924271624000200},
doi = {https://doi.org/10.1016/j.isprsjprs.2024.01.011},
issn = {0924-2716},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {209},
pages = {265-278},
abstract = {Land-cover information reflects basic Earth’s surface environments and is critical to human settlements. As a well-established deep learning architecture, the fully convolutional network has achieved impressive progress in various land-cover mapping tasks. However, most research has focused on designing powerful encoders, ignoring the exploration of decoders. The existing handcrafted decoders are relatively simple and lack flexibility, limiting the generalizability for complex remote sensing scenes. In this paper, we propose a Land-cOVEr mapping Neural Architecture Search framework (LoveNAS) to automatically find efficient decoders that are compatible with the encoders and tasks. Specifically, LoveNAS introduces a hierarchical dense search space, including densely connected layer-level and multi-scale operation-level search spaces. The search spaces contain independent connection and operation fusion strategies, facilitating sufficient interaction of multi-scale features. After searching based on large-scale datasets, a series of pre-trained encoders and adaptive decoders are obtained. These can be smoothly applied to multi-scene tasks using weight-transfer network training. Experimental results on normal and disaster scenes shows that LoveNAS outperforms 16 handcrafted architectures and NAS methods. Some searched structures coincide with the existing advanced artificial designs, revealing the potential value of LoveNAS in network design and guidance. Group’s website: http://rsidea.whu.edu.cn/resource_sharing.htm. GitHub page: https://github.com/Junjue-Wang/LoveNAS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Land-cover information reflects basic Earth’s surface environments and is critical to human settlements. As a well-established deep learning architecture, the fully convolutional network has achieved impressive progress in various land-cover mapping tasks. However, most research has focused on designing powerful encoders, ignoring the exploration of decoders. The existing handcrafted decoders are relatively simple and lack flexibility, limiting the generalizability for complex remote sensing scenes. In this paper, we propose a Land-cOVEr mapping Neural Architecture Search framework (LoveNAS) to automatically find efficient decoders that are compatible with the encoders and tasks. Specifically, LoveNAS introduces a hierarchical dense search space, including densely connected layer-level and multi-scale operation-level search spaces. The search spaces contain independent connection and operation fusion strategies, facilitating sufficient interaction of multi-scale features. After searching based on large-scale datasets, a series of pre-trained encoders and adaptive decoders are obtained. These can be smoothly applied to multi-scene tasks using weight-transfer network training. Experimental results on normal and disaster scenes shows that LoveNAS outperforms 16 handcrafted architectures and NAS methods. Some searched structures coincide with the existing advanced artificial designs, revealing the potential value of LoveNAS in network design and guidance. Group’s website: http://rsidea.whu.edu.cn/resource_sharing.htm. GitHub page: https://github.com/Junjue-Wang/LoveNAS.
3164.
Liu, Xin; Tian, Jie; Duan, Peiyong; Yu, Qian; Wang, Gaige; Wang, Yingjie
GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis Journal Article
In: Computers in Biology and Medicine, vol. 171, pp. 108118, 2024, ISSN: 0010-4825.
@article{LIU2024108118,
title = {GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis},
author = {Xin Liu and Jie Tian and Peiyong Duan and Qian Yu and Gaige Wang and Yingjie Wang},
url = {https://www.sciencedirect.com/science/article/pii/S0010482524002026},
doi = {https://doi.org/10.1016/j.compbiomed.2024.108118},
issn = {0010-4825},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computers in Biology and Medicine},
volume = {171},
pages = {108118},
abstract = {Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.
3163.
Yan, Jiaqi; Liu, Qianhui; Zhang, Malu; Feng, Lang; Ma, De; Li, Haizhou; Pan, Gang
Efficient spiking neural network design via neural architecture search Journal Article
In: Neural Networks, vol. 173, pp. 106172, 2024, ISSN: 0893-6080.
@article{YAN2024106172,
title = {Efficient spiking neural network design via neural architecture search},
author = {Jiaqi Yan and Qianhui Liu and Malu Zhang and Lang Feng and De Ma and Haizhou Li and Gang Pan},
url = {https://www.sciencedirect.com/science/article/pii/S0893608024000960},
doi = {https://doi.org/10.1016/j.neunet.2024.106172},
issn = {0893-6080},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Neural Networks},
volume = {173},
pages = {106172},
abstract = {Spiking neural networks (SNNs) are brain-inspired models that utilize discrete and sparse spikes to transmit information, thus having the property of energy efficiency. Recent advances in learning algorithms have greatly improved SNN performance due to the automation of feature engineering. While the choice of neural architecture plays a significant role in deep learning, the current SNN architectures are mainly designed manually, which is a time-consuming and error-prone process. In this paper, we propose a spiking neural architecture search (NAS) method that can automatically find efficient SNNs. To tackle the challenge of long search time faced by SNNs when utilizing NAS, the proposed NAS encodes candidate architectures in a branchless spiking supernet which significantly reduces the computation requirements in the search process. Considering that real-world tasks prefer efficient networks with optimal accuracy under a limited computational budget, we propose a Synaptic Operation (SynOps)-aware optimization to automatically find the computationally efficient subspace of the supernet. Experimental results show that, in less search time, our proposed NAS can find SNNs with higher accuracy and lower computational cost than state-of-the-art SNNs. We also conduct experiments to validate the search process and the trade-off between accuracy and computational cost.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spiking neural networks (SNNs) are brain-inspired models that utilize discrete and sparse spikes to transmit information, thus having the property of energy efficiency. Recent advances in learning algorithms have greatly improved SNN performance due to the automation of feature engineering. While the choice of neural architecture plays a significant role in deep learning, the current SNN architectures are mainly designed manually, which is a time-consuming and error-prone process. In this paper, we propose a spiking neural architecture search (NAS) method that can automatically find efficient SNNs. To tackle the challenge of long search time faced by SNNs when utilizing NAS, the proposed NAS encodes candidate architectures in a branchless spiking supernet which significantly reduces the computation requirements in the search process. Considering that real-world tasks prefer efficient networks with optimal accuracy under a limited computational budget, we propose a Synaptic Operation (SynOps)-aware optimization to automatically find the computationally efficient subspace of the supernet. Experimental results show that, in less search time, our proposed NAS can find SNNs with higher accuracy and lower computational cost than state-of-the-art SNNs. We also conduct experiments to validate the search process and the trade-off between accuracy and computational cost.
3162.
Qin, Ruiyang; Hu, Yuting; Yan, Zheyu; Xiong, Jinjun; Abbasi, Ahmed; Shi, Yiyu
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-06696,
title = {FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models},
author = {Ruiyang Qin and Yuting Hu and Zheyu Yan and Jinjun Xiong and Ahmed Abbasi and Yiyu Shi},
url = {https://doi.org/10.48550/arXiv.2402.06696},
doi = {10.48550/ARXIV.2402.06696},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.06696},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3161.
Vellenga, Koen; Steinhauer, H. Joe; Karlsson, Alexander; Falkman, Göran; Rhodin, Asli; Koppisetty, Ashok
Designing deep neural networks for driver intention recognition Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-05150,
title = {Designing deep neural networks for driver intention recognition},
author = {Koen Vellenga and H. Joe Steinhauer and Alexander Karlsson and Göran Falkman and Asli Rhodin and Ashok Koppisetty},
url = {https://doi.org/10.48550/arXiv.2402.05150},
doi = {10.48550/ARXIV.2402.05150},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.05150},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3160.
Hu, Liwei; Wang, Zidong; Li, Han; Wu, Peishu; Mao, Jingfeng; Zeng, Nianyin
ℓ-DARTS: Light-weight differentiable architecture search with robustness enhancement strategy Journal Article
In: Knowledge-Based Systems, vol. 288, pp. 111466, 2024, ISSN: 0950-7051.
@article{HU2024111466,
title = {ℓ-DARTS: Light-weight differentiable architecture search with robustness enhancement strategy},
author = {Liwei Hu and Zidong Wang and Han Li and Peishu Wu and Jingfeng Mao and Nianyin Zeng},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124001011},
doi = {https://doi.org/10.1016/j.knosys.2024.111466},
issn = {0950-7051},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Knowledge-Based Systems},
volume = {288},
pages = {111466},
abstract = {In this paper, a novel light-weight differentiable architecture search (ℓ-DARTS) model is proposed to address the challenge of balancing search efficiency and accuracy commonly faced in the neural architecture search (NAS) field. By reducing the model depth, the fast search on a simplified structure with less redundancy is facilitated by the proposed ℓ-DARTS as compared to the original DARTS. To bridge the discrepancy of semantic information among channels and mitigate potential accuracy degradation, a channel fusion compensation module is introduced. Furthermore, an enhanced regularization technique with a margin value is employed, which ensures thorough consideration of all candidate operations, thereby effectively reducing the preference for parameter-free operations during the search stage and consequently preventing performance collapse of the searched architecture. The proposed ℓ-DARTS is evaluated in various DARTS search spaces on three datasets, which achieves an accuracy of 97.54% ± 0.03 with 0.06 GPU-Days on CIFAR-10, and also demonstrates strong generalization on the target datasets CIFAR-100 and ImageNet with optimal accuracy, indicating its significant competitiveness against other leading DARTS variants. Moreover, the efficacy of the employed strategies is confirmed through extensive experiments, which promotes fair competition among candidate operations and favors the acquisition of a robust architecture.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, a novel light-weight differentiable architecture search (ℓ-DARTS) model is proposed to address the challenge of balancing search efficiency and accuracy commonly faced in the neural architecture search (NAS) field. By reducing the model depth, the fast search on a simplified structure with less redundancy is facilitated by the proposed ℓ-DARTS as compared to the original DARTS. To bridge the discrepancy of semantic information among channels and mitigate potential accuracy degradation, a channel fusion compensation module is introduced. Furthermore, an enhanced regularization technique with a margin value is employed, which ensures thorough consideration of all candidate operations, thereby effectively reducing the preference for parameter-free operations during the search stage and consequently preventing performance collapse of the searched architecture. The proposed ℓ-DARTS is evaluated in various DARTS search spaces on three datasets, which achieves an accuracy of 97.54% ± 0.03 with 0.06 GPU-Days on CIFAR-10, and also demonstrates strong generalization on the target datasets CIFAR-100 and ImageNet with optimal accuracy, indicating its significant competitiveness against other leading DARTS variants. Moreover, the efficacy of the employed strategies is confirmed through extensive experiments, which promotes fair competition among candidate operations and favors the acquisition of a robust architecture.
3159.
Xiang, Mingxi; Ding, Rui; Liu, Haijun; Zhou, Xichuan
Latency-Constrained Neural Architecture Search Method for Efficient Model Deployment on RISC-V Devices Journal Article
In: Electronics, vol. 13, no. 4, 2024, ISSN: 2079-9292.
@article{electronics13040692,
title = {Latency-Constrained Neural Architecture Search Method for Efficient Model Deployment on RISC-V Devices},
author = {Mingxi Xiang and Rui Ding and Haijun Liu and Xichuan Zhou},
url = {https://www.mdpi.com/2079-9292/13/4/692},
doi = {10.3390/electronics13040692},
issn = {2079-9292},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Electronics},
volume = {13},
number = {4},
abstract = {The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches.
3158.
Cao, Chunhong; Yi, Hongbo; Xiang, Han; He, Pan; Hu, Jing; Xiao, Fen; Gao, Xieping
Accelerated Sparse-Coding-Inspired Feedback Neural Architecture Search for Hyperspectral Image Classification Journal Article
In: IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024.
@article{10426762,
title = {Accelerated Sparse-Coding-Inspired Feedback Neural Architecture Search for Hyperspectral Image Classification},
author = {Chunhong Cao and Hongbo Yi and Han Xiang and Pan He and Jing Hu and Fen Xiao and Xieping Gao},
url = {https://ieeexplore.ieee.org/abstract/document/10426762},
doi = {10.1109/TGRS.2024.3363777},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {62},
pages = {1-14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3157.
Wu, Fan; Gao, Jinling; Hong, Lanqing; Wang, Xinbing; Zhou, Chenghu; Ye, Nanyang
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-04672,
title = {G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection},
author = {Fan Wu and Jinling Gao and Lanqing Hong and Xinbing Wang and Chenghu Zhou and Nanyang Ye},
url = {https://doi.org/10.48550/arXiv.2402.04672},
doi = {10.48550/ARXIV.2402.04672},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.04672},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3156.
Zeng, Junhua; Zhou, Guoxu; Li, Chao; Sun, Zhun; Zhao, Qibin
Discovering More Effective Tensor Network Structure Search Algorithms via Large Language Models (LLMs) Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-02456,
title = {Discovering More Effective Tensor Network Structure Search Algorithms via Large Language Models (LLMs)},
author = {Junhua Zeng and Guoxu Zhou and Chao Li and Zhun Sun and Qibin Zhao},
url = {https://doi.org/10.48550/arXiv.2402.02456},
doi = {10.48550/ARXIV.2402.02456},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.02456},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3155.
Dong, Peijie; Li, Lujun; Pan, Xinglin; Wei, Zimian; Liu, Xiang; Wang, Qiang; Chu, Xiaowen
ParZC: Parametric Zero-Cost Proxies for Efficient NAS Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-02105,
title = {ParZC: Parametric Zero-Cost Proxies for Efficient NAS},
author = {Peijie Dong and Lujun Li and Xinglin Pan and Zimian Wei and Xiang Liu and Qiang Wang and Xiaowen Chu},
url = {https://doi.org/10.48550/arXiv.2402.02105},
doi = {10.48550/ARXIV.2402.02105},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.02105},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3154.
Risso, Matteo; Xie, Chen; Daghero, Francesco; Burrello, Alessio; Mollaei, Seyedmorteza; Castellano, Marco; Macii, Enrico; Poncino, Massimo; Pagliari, Daniele Jahier
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-01226,
title = {HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays},
author = {Matteo Risso and Chen Xie and Francesco Daghero and Alessio Burrello and Seyedmorteza Mollaei and Marco Castellano and Enrico Macii and Massimo Poncino and Daniele Jahier Pagliari},
url = {https://doi.org/10.48550/arXiv.2402.01226},
doi = {10.48550/ARXIV.2402.01226},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.01226},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3153.
Zouambi, Meyssa
Optimizing Deep Learning: Navigating the Field of Neural Architecture Search from Theory to Practice PhD Thesis
2024.
@phdthesis{Zouambi-phd24a,
title = {Optimizing Deep Learning: Navigating the Field of Neural Architecture Search from Theory to Practice},
author = {Meyssa Zouambi},
url = {https://hal.science/tel-04437745/document},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
3152.
Zhang, Baochang; Wang, Tiancheng; Xu, Sheng; Doermann, David
Binary Neural Architecture Search Book Chapter
In: Neural Networks with Model Compression, pp. 49–99, Springer Nature Singapore, Singapore, 2024, ISBN: 978-981-99-5068-3.
@inbook{Zhang2024,
title = {Binary Neural Architecture Search},
author = {Baochang Zhang and Tiancheng Wang and Sheng Xu and David Doermann},
url = {https://doi.org/10.1007/978-981-99-5068-3_3},
doi = {10.1007/978-981-99-5068-3_3},
isbn = {978-981-99-5068-3},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Neural Networks with Model Compression},
pages = {49–99},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentation, and object detection. The success of DCNNs is attributed to effective architecture design. Neural architecture search (NAS) is an emerging approach that automates the process of designing neural architectures, replacing manual design.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentation, and object detection. The success of DCNNs is attributed to effective architecture design. Neural architecture search (NAS) is an emerging approach that automates the process of designing neural architectures, replacing manual design.
3151.
Tempel, Felix; Strümke, Inga; Ihlen, Espen Alexander Fürst
AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-01313,
title = {AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search},
author = {Felix Tempel and Inga Strümke and Espen Alexander Fürst Ihlen},
url = {https://doi.org/10.48550/arXiv.2402.01313},
doi = {10.48550/ARXIV.2402.01313},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.01313},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3150.
Zhang, Sheng; Wang, Maolin; Zhao, Yao; Zhuang, Chenyi; Gu, Jinjie; Guo, Ruocheng; Zhao, Xiangyu; Zhang, Zijian; Yin, Hongzhi
EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-00390,
title = {EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems},
author = {Sheng Zhang and Maolin Wang and Yao Zhao and Chenyi Zhuang and Jinjie Gu and Ruocheng Guo and Xiangyu Zhao and Zijian Zhang and Hongzhi Yin},
url = {https://doi.org/10.48550/arXiv.2402.00390},
doi = {10.48550/ARXIV.2402.00390},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.00390},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3149.
Pižurica, Nikola; Pavlović, Kosta; Kovačević, Slavko; Jovančević, Igor; Prado, Miguel
In: Journal of Electronic Imaging, vol. 33, no. 3, pp. 031203, 2024.
@article{10.1117/1.JEI.33.3.031203,
title = {Generic neural architecture search toolkit for efficient and real-world deployment of visual inspection convolutional neural networks in industry},
author = {Nikola Pižurica and Kosta Pavlović and Slavko Kovačević and Igor Jovančević and Miguel Prado},
url = {https://doi.org/10.1117/1.JEI.33.3.031203},
doi = {10.1117/1.JEI.33.3.031203},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Journal of Electronic Imaging},
volume = {33},
number = {3},
pages = {031203},
publisher = {SPIE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}