The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation
Open accessAbstract
Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search. However, these approaches often report lower accuracy in evaluating the searched architecture or transferring it to another dataset. This is arguably due to the large gap between the architecture depths in search and evaluation scenarios. In this paper, we present an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure. This brings two issues, namely, heavier computational overheads and weaker search stability, which we solve using search space approximation and regularization, respectively. With a significantly reduced search time (~7 hours on a single GPU), our approach achieves state-of-the-art performance on both the proxy dataset (CIFAR10 or CIFAR100) and the target dataset (ImageNet). Code is available at https://github.com/chenxin061/pdarts.
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Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
Chen, X., & Hsieh, C.-J. (2020) · arXiv · doi:10.48550/arxiv.2002.05283 · Cited by 126
Self-citationAdversarialNAS: Adversarial Neural Architecture Search for GANs
Gao, C., Chen, Y., Liu, S., Tan, Z., & Yan, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00572 · Cited by 111
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
Guo, J., Han, K., Wang, Y., Zhang, C., Yang, Z., Wu, H., Chen, X., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01142 · Cited by 110
Self-citationNeural Architecture Search Benchmarks: Insights and Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · IEEE Access · doi:10.1109/access.2023.3253818 · Cited by 64
Metadata
- Authors
- Chen, X., Xie, L., Wu, J., & Tian, Q.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 2019
- Pages
- 1294-1303
- OpenAlex
- W2981748264
- Cited by
- 666
- References
- 38
- FWCI
- 34.16
- Type
- conference paper
- Language
- en
- Open access
- green
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