4 accepted papers
Distillation strategies are currently the primary approaches for mitigating forgetting in class incremental learning (CIL). Existing methods generally inherit previous knowledge from a single teacher. However teachers with different mechanisms are talented at different tasks and inheriting diverse k…
Continual learning requires a model to incrementally learn a sequence of tasks and aims to predict well on all the learned tasks so far, which notoriously suffers from the catastrophic forgetting problem. In this paper, we find a new type of bias appearing in continual learning, coined as task-induc…
Self-supervised learning has attracted a lot of attention recently, which is able to learn powerful representations without any manual annotations. However, self-supervised learning needs to develop the ability to continuously learn to cope with a variety of real-world challenges, i.e., Continual Se…
Class incremental learning (CIL) is one of the most challenging scenarios in continual learning. Existing work mainly focuses on strategies like memory replay, regularization, or dynamic architecture but ignores a crucial aspect: mode connectivity. Recent studies have shown that different minima can…