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Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

20 Feb 2020ICML 2020 1arXiv:2002.08546archive 2025-07-28

Jian Liang, Dapeng Hu, Jiashi Feng

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model, making them risky and inefficient for decentralized private data. This work tackles a practical setting where only a trained source model is available and investigates how we can effectively utilize such a model without source data to solve UDA problems. We propose a simple yet generic representation learning framework, named \emph{Source HypOthesis Transfer} (SHOT). SHOT freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and self-supervised pseudo-labeling to implicitly align representations from the target domains to the source hypothesis. To verify its versatility, we evaluate SHOT in a variety of adaptation cases including closed-set, partial-set, and open-set domain adaptation. Experiments indicate that SHOT yields state-of-the-art results among multiple domain adaptation benchmarks.

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tim-learn/SHOT officialmentioned in papermentioned on GitHubpytorchMIT report
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Tasks

Domain AdaptationPartial Domain AdaptationRepresentation LearningSource-Free Domain AdaptationUniversal Domain AdaptationUnsupervised Domain Adaptation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Adaptation MNIST-to-USPS SHOT Accuracy 98.0 #6 of 14 Archive leaderboard report
Domain Adaptation Office-31 SHOT Average Accuracy 88.6 #22 of 40 Archive leaderboard report
Domain Adaptation Office-Home SHOT Accuracy 71.8 #19 of 29 Archive leaderboard report
Domain Adaptation SVHN-to-MNIST SHOT Accuracy 98.9 #2 of 14 Archive leaderboard report
Domain Adaptation SVNH-to-MNIST SHOT Accuracy 98.9 #2 of 9 Archive leaderboard report
Domain Adaptation USPS-to-MNIST SHOT Accuracy 98.4 #3 of 14 Archive leaderboard report
Domain Adaptation VisDA2017 SHOT Accuracy 82.9 #19 of 28 Archive leaderboard report
Partial Domain Adaptation Office-Home SHOT Accuracy (%) 78.3 #3 of 11 Archive leaderboard report
Source-Free Domain Adaptation VisDA-2017 SHOT Accuracy 82.9 #10 of 10 Archive leaderboard report
Universal Domain Adaptation DomainNet SHOT-O H-Score 32.6 #12 of 12 Archive leaderboard report
Universal Domain Adaptation DomainNet SHOT-O Source-free no #12 of 12 Archive leaderboard report
Universal Domain Adaptation Office-Home SHOT-O H-Score 40.7 #14 of 14 Archive leaderboard report
Universal Domain Adaptation Office-Home SHOT-O Source-free yes #14 of 14 Archive leaderboard report
Universal Domain Adaptation Office-Home SHOT-O VLM no #14 of 14 Archive leaderboard report
Universal Domain Adaptation VisDA2017 SHOT-O H-score 44.0 #11 of 13 Archive leaderboard report
Universal Domain Adaptation VisDA2017 SHOT-O Source-free yes #11 of 13 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.

Methods

Introduced by this paper: Source Hypothesis Transfer

Source Hypothesis Transfer

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