Domain adaptation refers to the problem of leveraging labeled task data in a source domain to learn an accurate model of the same tasks in a target domain where ...
Sep 26, 2019 · In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in ...
We summarize DA works most relevant to this paper. Several types of adversarial learning methods for unsuper- vised domain adaptation have been shown to match ...
In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in ...
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain. In this paper, ...
Jun 21, 2021 · In this paper, we propose an effective Task-oriented Alignment (ToAlign) for unsupervised domain adaptation (UDA).
In this paper, we propose an effective Task-oriented Alignment. (ToAlign) for unsupervised domain adaptation (UDA). We study what features should be aligned ...
Apr 15, 2021 · In domain adaptation, the key strategy is to overcome the shift between different domains and learn shared features with domain invariance.
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