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

• URL: http://arxiv.org/abs/2104.05309v1
• Date: Mon, 12 Apr 2021 09:32:33 GMT
• Title: Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
• Authors: Kaicheng Yu, Rene Ranftl, Mathieu Salzmann
• Abstract summary: Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. Recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. We propose a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone architectures.
• Score: 70.57382341642418
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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