Semantic-Guided Dynamic Sparsification for Pre-Trained Model-based Class-Incremental Learning
This addresses the challenge of continual learning for AI systems by improving adaptability without forgetting, though it is an incremental advance over existing adapter-based methods.
The paper tackles the problem of plasticity loss in class-incremental learning by proposing Semantic-Guided Dynamic Sparsification, which achieves state-of-the-art performance on benchmark datasets.
Class-Incremental Learning (CIL) requires a model to continually learn new classes without forgetting old ones. A common and efficient solution freezes a pre-trained model and employs lightweight adapters, whose parameters are often forced to be orthogonal to prevent inter-task interference. However, we argue that this parameter-constraining method is detrimental to plasticity. To this end, we propose Semantic-Guided Dynamic Sparsification (SGDS), a novel method that proactively guides the activation space by governing the orientation and rank of its subspaces through targeted sparsification. Specifically, SGDS promotes knowledge transfer by encouraging similar classes to share a compact activation subspace, while simultaneously preventing interference by assigning non-overlapping activation subspaces to dissimilar classes. By sculpting class-specific sparse subspaces in the activation space, SGDS effectively mitigates interference without imposing rigid constraints on the parameter space. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SGDS.