Single Path One-Shot Neural Architecture Search with Uniform Sampling
ECCV
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, Jian Sun
- Uniform Sampling
- One-shot
We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its advantages over existing NAS approaches. Existing one-shot method, however, is hard to train and not yet effective on large scale datasets like ImageNet. This work propose a Single Path One-Shot model to address the challenge in the training. Our central idea is to construct a simplified supernet, where all architectures are single paths so that weight co-adaption problem is alleviated. Training is performed by uniform path sampling. All architectures (and their weights) are trained fully and equally. Comprehensive experiments verify that our approach is flexible and effective. It is easy to train and fast to search. It effortlessly supports complex search spaces (e.g., building blocks, channel, mixed-precision quantization) and different search constraints (e.g., FLOPs, latency). It is thus convenient to use for various needs. It achieves start-of-the-art performance on the large dataset ImageNet.
@inproceedings{DBLP:conf/eccv/GuoZMHLWS20, author = {Zichao Guo and Xiangyu Zhang and Haoyuan Mu and Wen Heng and Zechun Liu and Yichen Wei and Jian Sun}, editor = {Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan{-}Michael Frahm}, title = {Single Path One-Shot Neural Architecture Search with Uniform Sampling}, booktitle = {Computer Vision - {ECCV} 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part {XVI}}, series = {Lecture Notes in Computer Science}, volume = {12361}, pages = {544--560}, publisher = {Springer}, year = {2020}, url = {https://doi.org/10.1007/978-3-030-58517-4\_32}, doi = {10.1007/978-3-030-58517-4_32}, timestamp = {Tue, 09 Feb 2021 15:29:44 +0100}, biburl = {https://dblp.org/rec/conf/eccv/GuoZMHLWS20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }