Generalized Class Incremental Learning

Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, Boi Faltings

Abstract: Many real-world machine learning systems require the ability to continually learn new knowledge. Class incremental learning receives increasing attention recently as a solution towards this goal. However, existing methods often introduce some assumptions to simplify the problem setting, which rarely holds in real-world scenarios. In this paper, we formulate a Generalized Class Incremental Learning (GCIL) framework to systematically alleviate these restrictions, and introduce several novel realistic incremental learning scenarios. In addition, we propose a simple yet effective method, namely ReMix, which combines Exemplar Replay (ER) and Mixup to deal with different challenges in realistic GCIL setups. We demonstrate on CIFAR-100 that ReMix outperforms the state-of-the-art methods in different GCIL setups by significant margins without introducing additional computation cost.