  Deep neural networks perform remarkably well in close-world scenarios.
However, novel classes emerged continually in real applications, making it
necessary to learn incrementally. Class-incremental learning (CIL) aims to
gradually recognize new classes while maintaining the discriminability of old
ones. Existing CIL methods have two limitations: a heavy reliance on preserving
old data for forgetting mitigation and the need for vast labeled data for
knowledge adaptation. To overcome these issues, we propose a non-exemplar
semi-supervised CIL framework with contrastive learning and semi-supervised
incremental prototype classifier (Semi-IPC). On the one hand, contrastive
learning helps the model learn rich representations, easing the trade-off
between learning representations of new classes and forgetting that of old
classes. On the other hand, Semi-IPC learns a prototype for each class with
unsupervised regularization, enabling the model to incrementally learn from
partially labeled new data while maintaining the knowledge of old classes.
Experiments on benchmark datasets demonstrate the strong performance of our
method: without storing any old samples and only using less than 1% of labels,
Semi-IPC outperforms advanced exemplar-based methods. We hope our work offers
new insights for future CIL research. The code will be made publicly available.
