PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks

ArXi:2605.19145v1 Announce Type: new In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learning new tasks leads to the loss of performance on previously learned tasks). Although all CL approaches use some form of memory to retain information about past tasks, a grounded understanding of what information needs to be d to minimize catastrophic forgetting remains elusive.