Network Architecture Search for Domain Adaptation

Yichen Li; Xingchao Peng

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arXiv · 2008.05706

Network Architecture Search for Domain Adaptation

Yichen Li·Xingchao Peng

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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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Yichen Li, Xingchao Peng. 2020-08-13. Network Architecture Search for Domain Adaptation. https://arxiv.org/abs/2008.05706

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