Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

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 …

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

Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the
only challenge. This paper argues for another challenge in class-incremental learning (CIL) …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Lifelong person re-identification (LReID) is in significant demand for real-world development
as a large amount of ReID data is captured from diverse locations over time and cannot be …

Catastrophic forgetting is the core problem of class incremental learning (CIL). Existing work
mainly adopts memory replay, knowledge distillation, and dynamic architecture to alleviate …

In neural networks, continual learning results in gradient interference among sequential
tasks, leading to catastrophic forgetting of old tasks while learning new ones. This issue is …