Papers › Self-ensembling for visual domain adaptation
Self-ensembling for visual domain adaptation
Geoffrey French, Michal Mackiewicz, Mark Fisher
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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | MNIST-to-USPS | Mean teacher | Accuracy | 98.26 | #4 of 14 | Archive leaderboard | report |
| Domain Adaptation | SVHN-to-MNIST | Mean teacher | Accuracy | 99.18 | #1 of 14 | Archive leaderboard | report |
| Domain Adaptation | Synth Signs-to-GTSRB | Mean teacher | Accuracy | 98.66 | #1 of 4 | Archive leaderboard | report |
| Domain Adaptation | USPS-to-MNIST | Mean teacher | Accuracy | 98.07 | #6 of 14 | Archive leaderboard | report |
| Domain Adaptation | VisDA2017 | Mean teacher | Accuracy | 85.4 | #17 of 28 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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