Self-ensembling for visual domain adaptation

Code

• github.com/Britefury/self-ensemble-visual-domain-adapt
  OfficialIn paperpytorch★ 0
• github.com/thuml/Transfer-Learning-Library
  pytorch★ 3,889
• github.com/domainadaptation/salad
  pytorch★ 339

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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Benchmark Results

Reproductions