  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, unfortunately, significantly increases the burden on storage
and transmission and further gives rise to a cumbersome model training process.
Besides, relying on the raw data for training \emph{per se} yields concerns
about privacy and copyright. To alleviate these shortcomings, dataset
distillation~(DD), also known as dataset condensation (DC), was introduced and
has recently attracted much research attention in the community. Given an
original dataset, DD aims to derive a much smaller dataset containing synthetic
samples, based on which the trained models yield performance comparable with
those trained on the original dataset. In this paper, we give a comprehensive
review and summary of recent advances in DD and its application. We first
introduce the task formally and propose an overall algorithmic framework
followed by all existing DD methods. Next, we provide a systematic taxonomy of
current methodologies in this area, and discuss their theoretical
interconnections. We also present current challenges in DD through extensive
experiments and envision possible directions for future works.
