As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

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 …

As deep learning models and datasets rapidly scale up, model training is extremely time-
consuming and resource-costly. Instead of training on the entire dataset, learning with a …

Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that
captures the rich information encoded in the original dataset. As the size of datasets …

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 …

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 …

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 …

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 …

The great success of machine learning with massive amounts of data comes at a price of
huge computation costs and storage for training and tuning. Recent studies on dataset …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …