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 …

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 distillation is the task of synthesizing a small dataset such that a model trained on
the synthetic set will match the test accuracy of the model trained on the full dataset. In this …

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

State-of-the-art deep neural networks are trained with large amounts (millions or even
billions) of data. The expensive computation and memory costs make it difficult to train them …

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 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 …