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 …

The ability of artificial agents to increment their capabilities when confronted with new data is
an open challenge in artificial intelligence. The main challenge faced in such cases is …

In this paper, we study dataset distillation (DD), from a novel perspective and introduce
a\emph {dataset factorization} approach, termed\emph {HaBa}, which is a plug-and-play …

Dataset condensation aims at reducing the network training effort through condensing a
cumbersome training set into a compact synthetic one. State-of-the-art approaches largely …

Computational cost of training state-of-the-art deep models in many learning problems is
rapidly increasing due to more sophisticated models and larger datasets. A recent promising …

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 …

Researchers have long tried to minimize training costs in deep learning while maintaining
strong generalization across diverse datasets. Emerging research on dataset distillation …

As the state-of-the-art machine learning methods in many fields rely on larger datasets,
storing datasets and training models on them become significantly more expensive. This …

Dataset distillation compresses large datasets into smaller synthetic coresets which retain
performance with the aim of reducing the storage and computational burden of processing …

In many machine learning problems, large-scale datasets have become the de-facto
standard to train state-of-the-art deep networks at the price of heavy computation load. In this …