Class-Incremental Learning with Strong Pre-trained Models

Class-incremental learning (CIL) has been widely stud-ied under the setting of starting from a small number of classes (base classes). Instead, we explore an understud-ied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypoth-esize that a strong base model can provide a good repre-sentation for novel classes and incremental learning can be done with small adaptations. We propose a 2-stage training scheme, i) feature augmentation - cloning part of the backbone and fine-tuning it on the novel data, and ii) fusion - combining the base and novel classifiers into a unified classifier. Experiments show that the proposed method sig-nificantly outperforms state-of-the-art CIL methods on the large-scale ImageNet dataset (e.g. + 10% overall accuracy than the best). We also propose and analyze understudied practical CIL scenarios, such as base-novel overlap with distribution shift. Our proposed method is robust and gen-eralizes to all analyzed CIL settings.