The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class
incremental learning (CIL) by preserving limited exemplars from previous tasks. With …

Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep
learning system to incrementally learn new classes with limited data. Recently, a pioneer …

A well-known issue for class-incremental learning is the catastrophic forgetting
phenomenon, where the network's recognition performance on old classes degrades …

The problem of class incremental learning (CIL) is considered. State-of-the-art approaches
use a dynamic architecture based on network expansion (NE), in which a task expert is …

Recent years have witnessed growing interests in developing deep models for incremental
learning. However, existing approaches often utilize the fixed structure and online …

Incremental learning with deep neural networks often suffers from catastrophic forgetting,
where newly learned patterns may completely erase the previous knowledge. A remedy is to …

Class-incremental learning aims to learn new classes in an incremental fashion without
forgetting the previously learned ones. Several research works have shown how additional …

Class-incremental learning (CIL) aims to train a classification model while the number of
classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity …

The ability to incrementally learn new classes is crucial to the development of real-world
artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot …

Abstract Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel
classes based on only few training samples, which poses a more challenging task than the …