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

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 …

A primary goal of class-incremental learning is to strike a balance between stability and
plasticity, where models should be both stable enough to retain knowledge learned from …

Federated learning (FL) is a hot collaborative training framework via aggregating model
parameters of decentralized local clients. However, most FL methods unreasonably assume …

Plasticity-stability dilemma is a main problem for incremental learning, where plasticity is
referring to the ability to learn new knowledge, and stability retains the knowledge of …

Lifelong learning has attracted much attention, but existing works still struggle to fight
catastrophic forgetting and accumulate knowledge over long stretches of incremental …

The incremental learning paradigm in machine learning has consistently been a focus of
academic research. It is similar to the way in which biological systems learn, and reduces …

Humans are capable of learning new tasks without forgetting previous ones, while neural
networks fail due to catastrophic forgetting between new and previously-learned tasks. We …

Federated Learning (FL) has been widely concerned for it enables decentralized learning
while ensuring data privacy. However, most existing methods unrealistically assume that the …

Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to
work on a budget. This is especially true when IL is modeled using a deep learning …