This paper proposed a new online continual learning approach called OCM based on
mutual information (MI) maximization. It achieves two objectives that are critical in dealing …

Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL
settings, class incremental learning (CIL) and task incremental learning (TIL). A major …

This paper studies class incremental learning (CIL) of continual learning (CL). Many
approaches have been proposed to deal with catastrophic forgetting (CF) in CIL. Most …

Online continual learning suffers from an underfitted solution due to insufficient training for
prompt model updates (eg single-epoch training). To address the challenge we propose an …

Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of
machine learning systems in the real world. Great progress has been made over the past …

Uncertainty-based deep learning models have attracted a great deal of interest for their
ability to provide accurate and reliable predictions. Evidential deep learning stands out …

As AI agents are increasingly used in the real open world with unknowns or novelties, they
need the ability to (1) recognize objects that (i) they have learned and (ii) detect items that …

Incremental learning models need to update the categories and their conceptual
understanding over time. The current research has placed more emphasis on learning new …

Class-incremental learning (CIL) is a particularly challenging variant of continual learning,
where the goal is to learn to discriminate between all classes presented in an incremental …

As more and more AI agents are used in practice, we need to think about how to make these
agents fully autonomous so that they can (1) learn by themselves continually in a self …