  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.
