Papers › Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

10 Mar 2019CVPR 2019 6arXiv:1903.04064archive 2025-07-28

Chen-Yu Lee, Tanmay Batra, Mohammad Haris Baig, Daniel Ulbricht

In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric. Our proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity between the outputs of task-specific classifiers. It provides a geometrically meaningful guidance to detect target samples that are far from the support of the source and enables efficient distribution alignment in an end-to-end trainable fashion. In the experiments, we validate the effectiveness and genericness of our method on digit and sign recognition, image classification, semantic segmentation, and object detection.

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Code

apple/ml-cvpr2019-swd mentioned on GitHubtfNOASSERTION report

kevinmusgrave/pytorch-adapt pytorch report

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Tasks

Domain Adaptation General Classification Image Classification Object Detection Semantic Segmentation Unsupervised Domain Adaptation image-classification object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Domain Adaptation	VisDA2017	SWD	Accuracy	76.4	#24 of 28	Archive leaderboard	report
Image-to-Image Translation	SYNTHIA-to-Cityscapes	SWD	mIoU (13 classes)	48.1	#19 of 28	Archive leaderboard	report
Synthetic-to-Real Translation	GTAV-to-Cityscapes Labels	SWD	mIoU	44.5	#61 of 73	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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