Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection

Recent paradigms in Random Projection Layer (RPL)–basedcontinual representation learning have demonstrated superior performancewhen building upon a pre-trained model (PTM). These methods insert arandomly initialized RPL after a PTM to enhance feature representationin the initial stage. Subsequently, a linear classification head is used foranalytic updates in the continual learning stage. However, under severedomain gaps between pre-trained representations and target domains, arandomly initialized RPL exhibits limited expressivity under large do-main shifts. While largely scaling up the RPL dimension can improveexpressivity, it also induces an ill-conditioned feature matrix, therebydestabilizing the recursive analytic updates of the linear head. To this end,we propose the Stochastic Continual Learner with MemoryGuard Super-visory Mechanism (SCL-MGSM). Unlike random initialization, MGSMconstructs the projection layer via a principled, data-guided mechanismthat progressively selects target-aligned random bases to adapt the PTMrepresentation to downstream tasks. This facilitates the construction of acompact yet expressive RPL while improving the numerical stability ofanalytic updates. Extensive experiments on multiple exemplar-free ClassIncremental Learning (CIL) benchmarks demonstrate that SCL-MGSMachieves superior performance compared to state-of-the-art methods.Project Page: https://rlinl.github.io/SCL/