Deep learning (DL) is a branch of machine learning and artificial intelligence that has been
applied to many areas in different domains such as health care and drug design. Cancer …

Test-time adaptation provides an effective means of tackling the potential distribution shift
between model training and inference, by dynamically updating the model at test time. This …

State-of-the-art deep neural networks are still struggling to address the catastrophic
forgetting problem in continual learning. In this paper, we propose one simple paradigm …

Deep learning systems typically suffer from catastrophic forgetting of past knowledge when
acquiring new skills continually. In this paper, we emphasize two dilemmas, representation …

The staple of human intelligence is the capability of acquiring knowledge in a continuous
fashion. In stark contrast, Deep Networks forget catastrophically and, for this reason, the sub …

Abstract Class Incremental Learning (CIL) aims at learning a classifier in a phase-by-phase
manner, in which only data of a subset of the classes are provided at each phase. Previous …

Both generalized and incremental few-shot learning have to deal with three major
challenges: learning novel classes from only few samples per class, preventing catastrophic …

This paper focuses on an under-explored yet important problem: Federated Class-Continual
Learning (FCCL), where new classes are dynamically added in federated learning. Existing …

Deep learning based visual cognition has greatly improved the accuracy of defect detection,
reducing processing times and increasing product throughput across a variety of …

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) …