class-incremental-learning

This paper proposes a lightweight plastic-memory framework for graph few-shot class-incremental learning, which uses an evolving micro-clustering structure and meta-learning to balance knowledge retention and adaptability to new classes with limited data.

This paper proposes FedFMX, a Fisher-Routed Mixture of Experts framework for Federated Class-Incremental Learning, addressing capacity conflict, catastrophic forgetting, and data heterogeneity via adaptive expert specialization.

This paper introduces CaRE, a novel continual learning framework using a bi-level routing mixture-of-experts mechanism to effectively handle class-incremental learning over sequences of 300+ tasks.