Driven by practical needs, research on Class-Incremental Learning (CIL) has received more
and more attentions in recent years. A technical challenge to be conquered by CIL methods …

Modern artificial intelligence systems require class-incremental learning while suffering from
catastrophic forgetting in many real-world applications. Due to the missing knowledge of …

Deep neural networks (DNNs) are prone to the notorious catastrophic forgetting problem
when learning new tasks incrementally. Class-incremental learning (CIL) is a promising …

Class-incremental learning (CIL) has achieved remarkable successes in learning new
classes consecutively while overcoming catastrophic forgetting on old categories. However …

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural
networks, where the performance decreases considerably while dealing with long …

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

Abstract Class-Incremental Learning (CIL) struggles with catastrophic forgetting when
learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging without …

This paper proposes two novel knowledge transfer techniques for class-incremental
learning (CIL). First, we propose data-free generative replay (DF-GR) to mitigate …

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 …

Abstract Class-Incremental Learning (CIL) aims to build classification models from data
streams. At each step of the CIL process, new classes must be integrated into the model …