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 …

Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …

Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …

Dataset distillation aims to learn a small synthetic dataset that preserves most of the
information from the original dataset. Dataset distillation can be formulated as a bi-level …

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; …

This paper proposed a new online continual learning approach called OCM based on
mutual information (MI) maximization. It achieves two objectives that are critical in dealing …

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 …

We discuss a general formulation for the Continual Learning (CL) problem for classification—
a learning task where a stream provides samples to a learner and the goal of the learner …

Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these
networks are trained on something new, they rapidly forget what was learned before. In the …

Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …