Memory-Enhanced Confidence Calibration for Class-Incremental Unsupervised Domain Adaptation

In this paper, we focus on Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain already includes all classes, and the classes in the unlabeled target domain emerge sequentially over time. This task involves addressing two main challenges. The first is the domain gap between the labeled source data and the unlabeled target data, which leads to weak generalization performance. The second is the inconsistency between the source and target category spaces at each time step, which causes catastrophic forgetting during the testing stage. Previous methods focus solely on the alignment of similar samples from different domains, which overlooks the underlying causes of the domain gap/class distribution difference. To tackle the issue, we rethink this task from a causal perspective for the first time. We first build a structural causal graph to describe the CI-UDA problem. Based on the causal graph, we present Memory-Enhanced Confidence Calibration (MECC), which aims to improve confidence in the predicted results. In particular, we argue that the domain discrepancy caused by the different styles is prone to make the model produce less confident predictions and thus weakens the generalization and continual learning abilities. To this end, we first explore using the gram matrix to generate source-style target data, which is combined with the original data to jointly train the model and thereby reduce the domain-shift impact. Second, we utilize the model of the previous time step to select corresponding samples that are used to build a memory bank, which is instrumental in alleviating catastrophic forgetting. Extensive experimental results on multiple datasets demonstrate the superiority of our method.