To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

Recent breakthroughs in self-supervised learning show that such algorithms learn visual
representations that can be transferred better to unseen tasks than cross-entropy based …

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 …

Online class-incremental continual learning is a specific task of continual learning. It aims to
continuously learn new classes from data stream and the samples of data stream are seen …

Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …

In the online continual learning paradigm, agents must learn from a changing distribution
while respecting memory and compute constraints. Experience Replay (ER), where a small …

Online continual learning (CL) studies the problem of learning continuously from a single-
pass data stream while adapting to new data and mitigating catastrophic forgetting …