Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Dataset Distillation is a newly emerging area that aims to distill large datasets into much
smaller and highly informative synthetic ones to accelerate training and reduce storage …

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 to condense a large dataset into a smaller one while
maintaining its ability to train a well-performing model, thus reducing the storage cost and …

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 …

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 …

Deep learning technology has developed unprecedentedly in the last decade and has
become the primary choice in many application domains. This progress is mainly attributed …

We present a new dataset condensation framework termed Squeeze, Recover and Relabel
(SRe $^ 2$ L) that decouples the bilevel optimization of model and synthetic data during …

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