Towards Non-Exemplar Semi-Supervised Class-Incremental Learning

Understand

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.