Few-shot Neural Architecture Search

Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo

Introduction

One-shot Neural Architecture Search uses a single supernet to approximate the performance each architecture. However, this performance estimation is super inaccurate because of co-adaption among operations in supernet. Few-shot NAS uses multiple supernets with less edges(operations) and each of them covers different regions of the search space to alleviate the undesired co-adaption. Compared to one-shot NAS, few-shot NAS greatly improve the performance of architecture evaluation with a small increase of overhead. Please click here to see our paper.

Paper

If you use the few-shot NAS data or code, please cite:

@InProceedings{pmlr-v139-zhao21d,
  title =    {Few-Shot Neural Architecture Search},
  author =       {Zhao, Yiyang and Wang, Linnan and Tian, Yuandong and Fonseca, Rodrigo and Guo, Tian},
  booktitle =    {Proceedings of the 38th International Conference on Machine Learning},
  pages =    {12707--12718},
  year =     {2021},
  volume =   {139},
  series =   {Proceedings of Machine Learning Research},
  month =    {18--24 Jul},
  publisher =    {PMLR},
  pdf =      {http://proceedings.mlr.press/v139/zhao21d/zhao21d.pdf},
  url =      {http://proceedings.mlr.press/v139/zhao21d.html},
}