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 distill an entire dataset's knowledge into a few synthetic images.
The idea is to synthesize a small number of synthetic data points that, when given to a …

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 …

Abstract Model-based deep learning has achieved astounding successes due in part to the
availability of large-scale real-world data. However, processing such massive amounts of …

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 …

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

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 …

In this paper, we propose a novel data-pruning approach called moving-one-sample-out
(MoSo), which aims to identify and remove the least informative samples from the training …

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