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 …

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 …

Dataset distillation aims to synthesize small datasets with little information loss from original
large-scale ones for reducing storage and training costs. Recent state-of-the-art methods …

Contemporary machine learning which involves training large neural networks on massive
datasets faces significant computational challenges. Dataset distillation as a recent …

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 …

Dataset distillation, also known as dataset condensation, aims to compress a large dataset
into a compact synthetic one. Existing methods perform dataset condensation by assuming a …

The popularity of deep learning has led to the curation of a vast number of massive and
multifarious datasets. Despite having close-to-human performance on individual tasks …

The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a
model trained on this synthetic set will perform equally well as a model trained on the full …

Dataset distillation is a newly emerging task that synthesizes a small-size dataset used in
training deep neural networks (DNNs) for reducing data storage and model training costs …

Dataset distillation reduces the storage and computational consumption of training a
network by generating a small surrogate dataset that encapsulates rich information of the …