Popular deep-learning models in the field of image classification suffer from catastrophic
forgetting—models will forget previously acquired skills when learning new ones …

Abstract We introduce Domain-Adaptive Prompt (DAP), a novel method for continual
learning using Vision Transformers (ViT). Prompt-based continual learning has recently …

A major obstacle to developing artificial intelligence applications capable of true lifelong
learning is that artificial neural networks quickly or catastrophically forget previously learned …

The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting is the central …

Online class-incremental continual learning (CL) studies the problem of learning new
classes continually from an online non-stationary data stream, intending to adapt to new …

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

Retaining a small subset to replay is a direct and effective way to prevent catastrophic
forgetting in continual learning. However, due to data complexity and restricted memory …

Learning from non-stationary data streams and overcoming catastrophic forgetting still
poses a serious challenge for machine learning research. Rather than aiming to improve …

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 …

In this work, we improve the generative replay in a continual learning setting. We notice that
in VAE-based generative replay, the generated features are quite far from the original ones …