The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Searching for a Robust Neural Architecture in Four GPU Hours
Open accessAbstract
Conventional neural architecture search (NAS) approaches are usually based on reinforcement learning or evolutionary strategy, which take more than 1000 GPU hours to find a good model on CIFAR-10. We propose an efficient NAS approach, which learns the searching approach by gradient descent. Our approach represents the search space as a directed acyclic graph (DAG). This DAG contains thousands of sub-graphs, each of which indicates a kind of neural architecture. To avoid traversing all the possibilities of the sub-graphs, we develop a differentiable sampler over the DAG. This sampler is learnable and optimized by the validation loss after training the sampled architecture. In this way, our approach can be trained in an end-to-end fashion by gradient descent, named Gradient-based search using Differentiable Architecture Sampler (GDAS). In experiments, we can finish one searching procedure in four GPU hours on CIFAR-10, and the discovered model obtains a test error of 2.82% with only 2.5M parameters, which is on par with the state-of-the-art.
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Chen, X., Xie, L., Wu, J., & Tian, Q. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00138 · Cited by 666
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
Chu, X., Zhang, B., & Xu, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01202 · Cited by 322
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., & Yang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00208 · Cited by 262
Self-citationStabilizing Differentiable Architecture Search via Perturbation-based Regularization
Chen, X., & Hsieh, C.-J. (2020) · arXiv · doi:10.48550/arxiv.2002.05283 · Cited by 126
Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118
Metadata
- Authors
- Dong, X., & Yang, Y.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1761-1770
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2960010704
- Cited by
- 640
- References
- 42
- FWCI
- 32.81
- Type
- conference paper
- Language
- en
- Open access
- green
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