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 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 …

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 at reducing the network training effort through condensing a
cumbersome training set into a compact synthetic one. State-of-the-art approaches largely …

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 …

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 …

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 …

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 …