Self-ensembling for visual domain adaptation

Geoffrey French; Michal Mackiewicz; Mark Fisher

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arXiv · 1706.05208

Self-ensembling for visual domain adaptation

Geoffrey French·Michal Mackiewicz·Mark Fisher

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Abstract

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.

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Geoffrey French, Michal Mackiewicz, Mark Fisher. 2017-06-16. Self-ensembling for visual domain adaptation. https://arxiv.org/abs/1706.05208

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