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IEEE Transactions on Neural Networks and Learning SystemsStrong priors are imposed on the search space of Differentiable Architecture Search (DARTS), such that cells of the same type share the same topological structure and each intermediate node retains two operators from distinct nodes. While these priors reduce optimization difficulties and improve the applicability of searched architectures, they hinder ...
Bo Zhao, Derong Liu, Cesare Alippi
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Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search [PDF]
Lecture Notes in Computer Science, 2020 Differentiable Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method. However, it suffers from well-known performance collapse due to an inevitable aggregation of skip connections. In this paper, we first disclose that its root cause lies in an unfair advantage in exclusive competition.
Jixiang Li, Tianbao Zhou, Bo Zhang
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EPC-DARTS: Efficient Partial Channel Connection for Differentiable Architecture Search
Neural Networks, 2023With weight-sharing and continuous relaxation strategies, the differentiable architecture search (DARTS) proposes a fast and effective solution to perform neural network architecture search in various deep learning tasks. However, unresolved issues, such as the inefficient memory utilization, and the poor stability of the search architecture due to ...
Hai-Lin Liu
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MR-DARTS: Restricted Connectivity Differentiable Architecture Search in Multi-Path Search Space
Neurocomputing, 2022Hao Qin, Bin Song
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Att-DARTS: Differentiable Neural Architecture Search for Attention
2020 International Joint Conference on Neural Networks (IJCNN), 2020Neural architecture search (NAS) is a promising method to automatically identify neural network architectures. Differentiable architecture search (DARTS) is method that significantly reduces search time and finds architectures that can achieve state-of-the-art performance.
Kuniaki Uehara, Takashi Matsubara
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A-DARTS: attention-guided differentiable architecture search for lung nodule classification
Journal of Electronic Imaging, 2021Lung cancer has caused the most cancer deaths in the past several years. Benign–malignant lung nodule classification is vital in lung nodule detection, which can help early diagnosis of lung cancer. Most existing works extract the features of chest CT images using the well-designed networks, which require substantial effort of experts.
Liangxiao Hu +5 more
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PD-DARTS: Progressive Discretization Differentiable Architecture Search
2020Architecture design is a crucial step for neural-network-based methods, and it requires years of experience and extensive work. Encouragingly, with recently proposed neural architecture search (NAS), the architecture design process could be automated. In particular, differentiable architecture search (DARTS) reduces the time cost of search to a couple ...
Yonggang Li +3 more
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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Hai-Lin Liu
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Hai-Lin Liu
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E-DARTS: Enhanced Differentiable Architecture Search for Acoustic Scene Classification
2021 16th International Conference on Computer Engineering and Systems (ICCES), 2021Ali Saudi +2 more
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F-DARTS: Foveated Differentiable Architecture Search Based Multimodal Medical Image Fusion
IEEE Transactions on Medical Imaging, 2023Multimodal medical image fusion (MMIF) is highly significant in such fields as disease diagnosis and treatment. The traditional MMIF methods are difficult to provide satisfactory fusion accuracy and robustness due to the influence of such possible human-crafted components as image transform and fusion strategies.
Shaozhuang Ye +3 more
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