Mnemonics Training: Multi-Class Incremental Learning Without Forgetting

Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and exemplar-level. We conduct extensive experiments on three MCIL benchmarks, CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics exemplars can surpass the state-of-the-art by a large margin. Interestingly and quite intriguingly, the mnemonics exemplars tend to be on the boundaries between different classes 1 . * This work was done during Yaoyao's internship supervised by Qianru. † Corresponding authors. 1 Code: https://github.com/yaoyao-liu/mnemonics-training random (baseline) herding (related) mnemonics (ours) Early phase (50 classes used, 5 classes visualized in color): Late phase (100 classes used, 5 classes visualized in color):