Adaptive Discovering and Merging for Incremental Novel Class Discovery

The author introduces a paradigm called Adaptive Discovering and Merging (ADM) to address the challenges of discovering novel classes adaptively while mitigating catastrophic forgetting. The approach involves decoupling representation learning, using Triple Comparison (TC) and Probability Regularization (PR), and proposing Adaptive Model Merging (AMM).