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 …

Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

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 …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

Continual learning aims to empower artificial intelligence with strong adaptability to the real
world. For this purpose, a desirable solution should properly balance memory stability with …

Abstract According to Complementary Learning Systems (CLS) theory~\cite
{mcclelland1995there} in neuroscience, humans do effective\emph {continual learning} …

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

Bilevel optimization (BO) is useful for solving a variety of important machine learning
problems including but not limited to hyperparameter optimization, meta-learning, continual …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …