Self-ensembling for domain adaptation

نویسندگان

  • Geoffrey French
  • Michal Mackiewicz
  • Mark H. Fisher
چکیده

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant [20] of temporal ensembling [8], 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 [12]. 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

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عنوان ژورنال:
  • CoRR

دوره abs/1706.05208  شماره 

صفحات  -

تاریخ انتشار 2017