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 …

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

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

Class-incremental learning (CIL) aims to enable models to continuously learn new classes
while overcoming catastrophic forgetting. The introduction of pre-trained models has brought …

Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from
very few data (5 samples) without forgetting the previously learned classes. Recent works in …

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 …