Network Architecture Search for Domain Adaptation

• URL: http://arxiv.org/abs/2008.05706v1
• Date: Thu, 13 Aug 2020 06:15:57 GMT
• Title: Network Architecture Search for Domain Adaptation
• Authors: Yichen Li, Xingchao Peng
• Abstract summary: We present Neural Architecture Search for Domain Adaptation (NASDA), a principle framework that leverages differentiable neural architecture search to derive the optimal network architecture for domain adaptation task. We demonstrate experimentally that NASDA leads to state-of-the-art performance on several domain adaptation benchmarks.
• Score: 11.24426822697648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep networks have been used to learn transferable representations for domain adaptation. Existing deep domain adaptation methods systematically employ popular hand-crafted networks designed specifically for image-classification tasks, leading to sub-optimal domain adaptation performance. In this paper, we present Neural Architecture Search for Domain Adaptation (NASDA), a principle framework that leverages differentiable neural architecture search to derive the optimal network architecture for domain adaptation task. NASDA is designed with two novel training strategies: neural architecture search with multi-kernel Maximum Mean Discrepancy to derive the optimal architecture, and adversarial training between a feature generator and a batch of classifiers to consolidate the feature generator. We demonstrate experimentally that NASDA leads to state-of-the-art performance on several domain adaptation benchmarks.

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