Vicinal and categorical domain adaptation | 数据学习(DataLearner)

Highlights：

• We propose to use vicinal domains to augment the alignment of original domains. We present novel adversarial losses for vicinal domain adaptation based on the above designs, giving rise to Vicinal and Categorical Domain Adaptation.

• To recover the intrinsic target discrimination damaged by adversarial feature alignment, we propose Target Discriminative Structure Recovery, which fine-tunes the trained model by semantically anchored spherical k-means.

• We analyze the working mechanisms of our key designs in principle. Particularly, we explain our cross-domain weighting scheme by connecting it with information theory and optimization equilibrium.

• We achieve the new state of the art on several commonly used benchmark datasets.

摘要

•We propose novel adversarial losses at multiple levels on both the source and target domains to promote categorical domain adaptation. Based on a joint domain-category classifier, the category-level adversarial loss improves over the domain-level one by a heterogenous, cross-domain weighting design.•We propose to use vicinal domains to augment the alignment of original domains. We present novel adversarial losses for vicinal domain adaptation based on the above designs, giving rise to Vicinal and Categorical Domain Adaptation.•To recover the intrinsic target discrimination damaged by adversarial feature alignment, we propose Target Discriminative Structure Recovery, which fine-tunes the trained model by semantically anchored spherical k-means.•We analyze the working mechanisms of our key designs in principle. Particularly, we explain our cross-domain weighting scheme by connecting it with information theory and optimization equilibrium.•We achieve the new state of the art on several commonly used benchmark datasets.