Continual learning aims to enable a single model to learn a sequence of tasks without
catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store …

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

Continual Learning aims to learn a single model on a sequence of tasks without having
access to data from previous tasks. The biggest challenge in the domain still remains …

Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …

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

Continual Learning (CL) methods aim to enable machine learning models to learn new
tasks without catastrophic forgetting of those that have been previously mastered. Existing …

Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously
acquired knowledge. However, recent CL advances are restricted to supervised continual …

Continual learning aims to learn on non-stationary data streams without catastrophically
forgetting previous knowledge. Prevalent replay-based methods address this challenge by …

The continual learning setting aims to learn new tasks over time without forgetting the
previous ones. The literature reports several significant efforts to tackle this problem with …

In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …