Papers › Multi-Adversarial Domain Adaptation

Multi-Adversarial Domain Adaptation

4 Sep 2018arXiv:1809.02176archive 2025-07-28

Zhongyi Pei, Zhangjie Cao, Mingsheng Long, Jian-Min Wang

Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain adversarial adaptation methods based on single domain discriminator only align the source and target data distributions without exploiting the complex multimode structures. In this paper, we present a multi-adversarial domain adaptation (MADA) approach, which captures multimode structures to enable fine-grained alignment of different data distributions based on multiple domain discriminators. The adaptation can be achieved by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Empirical evidence demonstrates that the proposed model outperforms state of the art methods on standard domain adaptation datasets.

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MarvinMartin24/MADA-PL mentioned on GitHubpytorch report
arthurdouillard/mada.pytorch mentioned on GitHubpytorchMIT report
cht619/MADA mentioned on GitHubpytorch report

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Domain Adaptation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Domain Adaptation Office-31 MADA Average Accuracy 85.2 #30 of 40 Archive leaderboard report

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