  Dataset distillation is attracting more attention in machine learning as
training sets continue to grow and the cost of training state-of-the-art models
becomes increasingly high. By synthesizing datasets with high information
density, dataset distillation offers a range of potential applications,
including support for continual learning, neural architecture search, and
privacy protection. Despite recent advances, we lack a holistic understanding
of the approaches and applications. Our survey aims to bridge this gap by first
proposing a taxonomy of dataset distillation, characterizing existing
approaches, and then systematically reviewing the data modalities, and related
applications. In addition, we summarize the challenges and discuss future
directions for this field of research.
