Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain Adaptation

Domain adaptation (DA) aims to transfer knowledge from a label-rich source domain to a related but labelscarce target domain. Recently, increasing research has focused on exploring data structure of the target domain. In light of the recent success of Instance Discrimination Contrastive (IDCo) loss in self-supervised learning, we try directly applying it to domain adaptation tasks. However, the improvement is very limited, which motivates us to rethink its underlying limitations for domain adaptation tasks. An intuitive limitation is that a pair of samples belonging to the same class could be treated as negatives. Here we argue that using low-confidence samples to construct positive and negative pairs can alleviate this issue and is more suitable for IDCo loss. Another limitation is that IDCo loss cannot capture enough semantic information. We address this by introducing domain-invariant and accurate semantic information from classifier weights and input data. Specifically, we propose a class relationship enhanced features. It uses probability weighted class prototpyes as the input features of IDCo loss, which can implicitly transfer the domaininvariant class relationship. We further propose a targetdominated cross-domain mixup that can incorporate accurate semantic information from the source domain. We evaluate the proposed method in unsupervised DA and other DA settings, and extensive experimental results reveal that our method can make IDCo loss more effective and achieve state-of-the-art performance. 1 * Corresponding author 1 Code is available at https://github.com/zhyx12/EIDCo Class Relationship Enhanced Features Sample feature Classifier weight Class 1 Class 2