5 accepted papers
The majority of online continual learning (CL) advocates single-epoch training and imposes restrictions on the size of replay memory. However, single-epoch training would incur a different amount of computations per CL algorithm, and the additional storage cost to store logit or model in addition to…
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model updates (e.g. single-epoch training). To address the challenge we propose an efficient online continual learning method using the neural collapse phenomenon. In particular we induce neural co…
Typical continual learning setup assumes that the dataset is split into multiple discrete tasks. We argue that it is less realistic as the streamed data would have no notion of task boundary in real-world data. Here, we take a step forward to investigate more realistic online continual learning – le…
Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups. While a handful of work do propose new continual learning setups, they still lack practicality in certain aspects. For better practicality, we first propose a novel cont…
Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. Large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplor…