Contrastive Domain Adaptation Via Delimitation Discriminator

Abstract

Unsupervised domain adaptation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, thereby improving the classification performance of the target domain. Recent methods use contrastive learning to optimize this task, however, these methods only focus on contrastive learning for aspects of domain alignment, which do not actively serve the classification task, resulting in suboptimal solution. To this end, we proposed contrastive domain adaptation via delimitation discriminator (CDVD), which addresses the inconsistency problem of optimizing contrastive learning and classification tasks. We introduce a delimitation discriminator to maximize the output difference between two types of random data-augmented samples of the target domain to detect data-augmented samples that are not conducive to classification, and then minimize the difference through the feature generator to generate effective data-augmented features (good for classification). Our extensive experiments on several public datasets show that CDVD has good adaptability and effectiveness.