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 …

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 …

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …

Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a
number of participants without sharing data. Recent works have begun to consider the …

Foundation models encompass an extensive knowledge base and offer remarkable
transferability. However this knowledge becomes outdated or insufficient over time. The …

Abstract Class-Incremental Learning (CIL) aims to build classification models from data
streams. At each step of the CIL process, new classes must be integrated into the model …

We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL)
algorithms through the metric of online accuracy, which measures the accuracy of the model …

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 …