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- W4315705977 abstract "Recent advances in domain adaptation reveal that adversarial learning on deep neural networks can learn domain invariant features to reduce the shift between source and target domains. While such adversarial approaches achieve domain-level alignment, they ignore the class (label) shift. When class-conditional data distributions are significantly different between the source and target domain, it can generate ambiguous features near class boundaries that are more likely to be misclassified. In this work, we propose a two-stage model for domain adaptation called textbf{C}ontrastive-adversarial textbf{D}omain textbf{A}daptation textbf{(CDA)}. While the adversarial component facilitates domain-level alignment, two-stage contrastive learning exploits class information to achieve higher intra-class compactness across domains resulting in well-separated decision boundaries. Furthermore, the proposed contrastive framework is designed as a plug-and-play module that can be easily embedded with existing adversarial methods for domain adaptation. We conduct experiments on two widely used benchmark datasets for domain adaptation, namely, textit{Office-31} and textit{Digits-5}, and demonstrate that CDA achieves state-of-the-art results on both datasets." @default.
- W4315705977 created "2023-01-12" @default.
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- W4315705977 date "2023-01-10" @default.
- W4315705977 modified "2023-09-29" @default.
- W4315705977 title "CDA: Contrastive-adversarial Domain Adaptation" @default.
- W4315705977 doi "https://doi.org/10.48550/arxiv.2301.03826" @default.
- W4315705977 hasPublicationYear "2023" @default.
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