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 …

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 …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

For future learning systems, incremental learning is desirable because it allows for: efficient
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …

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 is a realistic learning scenario for AI models. Prevalent scenario of
continual learning, however, assumes disjoint sets of classes as tasks and is less realistic …

Averaging the parameters of models that have the same architecture and initialization can
provide a means of combining their respective capabilities. In this paper, we take the …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

The ability to incrementally learn new classes is crucial to the development of real-world
artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot …

Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes
continually from limited samples without forgetting the old classes. The mainstream …