Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. This violates the main assumption of weight-sharing NAS algorithms, thus limiting their effectiveness. We tackle this issue by proposing a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone architectures using a small set of landmark architectures. We incorporate our regularization term into three different NAS algorithms and show that it consistently improves performance across algorithms, search-spaces, and tasks.
WOS:000742075003091
2021-01-01
978-1-6654-4509-2
Los Alamitos
IEEE Conference on Computer Vision and Pattern Recognition
13718
13727
REVIEWED
Event name | Event place | Event date |
ELECTR NETWORK | Jun 19-25, 2021 | |