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arXiv · 2104.05309

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Kaicheng Yu·Rene Ranftl·Mathieu Salzmann
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Abstract

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.

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Kaicheng Yu, Rene Ranftl, Mathieu Salzmann. 2021-04-12. Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search. https://arxiv.org/abs/2104.05309

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