Self-ensembling for visual domain adaptation
This addresses domain shift problems in computer vision, but it is incremental as it modifies existing techniques for adaptation scenarios.
The paper tackles visual domain adaptation by adapting self-ensembling methods, achieving state-of-the-art results in benchmarks like VISDA-2017 and close to supervised accuracy in small image tasks.
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.