Class Incremental Learning With Large Domain Shift - Kamin Lee; Hyoeun Kim; Geunjae Choi; Hyejeong Jeon; Nojun Kwak (IEEE Access, 2024)
We address an important and practical problem facing deep-learning-based image classification: class incremental learning with a large domain shift. Most previous efforts on class incremental learning focus on one aspect of the problem, i.e., learning to classify additional new classes (with a little shift). However, in the real world, when new classes are added, the domain changes simultaneously (with a large domain shift). To obtain a model that is robust to these situations, we need to consider incrementally learning not only new labels but also domain-shifted labels. We target a continual and simultaneous shift of class and domain distribution and propose a new incremental learning method named Momentum Contrastive learning enhancing Orthogonality of Negative pairs (MoCo-ON). We employ a momentum encoder framework augmented with rehearsal memory to mitigate the risk of forgetting while leveraging contrastive learning to extract versatile features capable of adapting to the progressively shifting domain. Specifically, when training with a knowledge distillation loss, we introduce a novel supervised contrastive loss designed to closely embed positive pairs of the same class, even in the presence of a substantial domain gap. Additionally, we leverage feature embedding from momentum encoders for exemplar selection, aiming to mitigate the risk of forgetting previously acquired information from earlier tasks. We conduct comprehensive experiments involving inter-domain shifted class incremental learning scenarios using widely adopted datasets commonly employed for studying domain generalization in image classification. Our proposed method consistently outperforms other methods by a significant margin.