2026
Random Amalgamation of Adapters for Flatter Loss Landscapes: Towards Class-Incremental Learning with Better Stability
AAAI 2026technical
Class-incremental learning (CIL) enables models to continuously learn from streaming data while mitigating catastrophic forgetting of prior knowledge. Our research reveals that the CIL performance of pre-trained models (PTMs) varies significantly across different datasets, a phenomenon underexplored