Non-exemplar Domain Incremental Learning via Cross-Domain Concept Integration

Existing approaches to Domain Incremental Learning (DIL) address catastrophic forgetting by storing and rehearsing exemplars from old domains.However, exemplar-based solutions are not always viable due to data privacy concerns or storage limitations.Therefore, Non-Exemplar Domain Incremental Learning (NEDIL) has emerged as a significant paradigm for resolving DIL challenges.Current NEDIL solutions extend the classifier incrementally for new domains to learn new knowledge, but unrestricted extension within the same feature space leads to inter-class confusion.To tackle this issue, we propose a simple yet effective method through cross-domain concePt INtegrAtion (PINA).We train a Unified Classifier (UC) as a concept container across all domains.Then, a Domain Specific Alignment (DSA) module is proposed for each incremental domain, aligning the feature distribution to the base domain.During inference, we introduce a Patch Shuffle Selector (PSS) to select appropriate parameters of DSA for test images. Our developed patch shuffling technique disrupts class-dependent information, outperforming the domain selectors based on K-Nearest Neighbors or Nearest Mean Classifier.Extensive experiments demonstrate that our method achieves state-of-the-art performance while reducing the number of additional parameters. The source code will be released in https://github.com/qwangcv/PINA.