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The most famous dilemma for class incremental learning(CIL) is the catastrophic forgetting phenomenon, where the trained model quickly forgets to recognize the old classes when learning novel ones. To cope with this problem, we propose a novel max-margin class incremental learning with less forgetting, which can well discriminate the relationship between classes and prevent knowledge forgetting in the incremental learning process. Specifically, we introduce a maxmargin objective function to explicitly enforce the maximum distance between different classes larger than a predefined margin, which can avoid the ambiguity between old and new classes and then increase the discriminative ability of the learned classifier. Further, we adopt a mixup augmentation mechanism by performing the mixup operation on the reserved images of old classes and the incremental images of new classes, especially to reduce knowledge forgetting. Comprehensive evaluations and comparisons on three public datasets (including CIFAR-100, ImageNet-Subset and ImageNet) well demonstrate that our proposed CIL approach can effectively improve performance compared to the existing CIL methods.
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