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 …

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 …

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 …

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 …

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 …

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …

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 is a method for reducing dataset sizes by learning a small number of
representative synthetic samples. This has several benefits such as speeding up model …