To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Prompt-based continual learning is an emerging direction in leveraging pre-trained
knowledge for downstream continual learning, and has almost reached the performance …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

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 …

Continual learning is a subfield of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …

Traditional online continual learning (OCL) research has primarily focused on mitigating
catastrophic forgetting with fixed and limited storage allocation throughout an agent's …

Exemplar-free class-incremental learning using a backbone trained from scratch and
starting from a small first task presents a significant challenge for continual representation …

In this work, we study the problem of continual learning (CL) where the goal is to learn a
model on a sequence of tasks, under the assumption that the data from the previous tasks …

The deployment of pre-trained models (PTMs) has greatly advanced the field of continual
learning (CL), enabling positive knowledge transfer and resilience to catastrophic forgetting …

Many widely used continual learning benchmarks follow a protocol that starts from an
untrained, randomly initialized model that needs to sequentially learn a number of incoming …