Learning to Match Distributions for Domain Adaptation

17 Jul 2020Chaohui YuJindong WangChang LiuTao QinRenjun XuWenjie FengYiqiang ChenTie-Yan Liu

When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is difficult to directly match the cross-domain joint distributions, existing methods tend to reduce the marginal or conditional distribution divergence using predefined distances such as MMD and adversarial-based discrepancies... (read more)

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