One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

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 …

Recent years have seen significant progress in unsupervised continual learning methods.
Despite their success in controlled settings, their practicality in real-world contexts remains …

Online continual learning suffers from an underfitted solution due to insufficient training for
prompt model updates (eg single-epoch training). To address the challenge we propose an …

In recent years, online distillation has emerged as a powerful technique for adapting real-
time deep neural networks on the fly using a slow, but accurate teacher model. However, a …

State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision
Transformers to learn task-specific prompts, drastically reducing catastrophic forgetting …

Abstract Foundation Models (FMs) have become the hallmark of modern AI however these
models are trained on massive data leading to financially expensive training. Updating FMs …

In real-world scenarios, extensive manual annotation for continual learning is impractical
due to prohibitive costs. Although prior arts, influenced by large-scale webly supervised …

We propose and study a realistic Continual Learning (CL) setting where learning algorithms
are granted a restricted computational budget per time step while training. We apply this …

Anomaly detection is of paramount importance in many real-world domains characterized by
evolving behavior, such as monitoring cyber-physical systems, human conditions and …