Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection
Ruilin Li, Heming Zou, Xiufeng Yan, et al.
This paper addresses a problem in continual learning (where AI models learn new tasks sequentially without forgetting old ones) by improving how randomly initialized projection layers work with pre-trained models. The authors propose SCL-MGSM, a method that intelligently selects which random features to use rather than using purely random ones, making the system more stable and accurate when learning new tasks with limited examples.
continual learningclass incremental learningrandom projectionpre-trained models