Venue: arXiv
Year: 2023
Paper: https://arxiv.org/abs/2301.05603
Abstract
Deep learning technology has unprecedentedly developed in the last decade and has become the primary choice in many application domains. This progress is mainly attributed to a systematic collaboration that rapidly growing computing resources encourage advanced algorithms to deal with massive data. However, it gradually becomes challenging to cope with the unlimited growth of data with limited computing power. To this end, diverse approaches are proposed to improve data processing efficiency. Dataset distillation, one of the dataset reduction methods, tackles the problem via synthesising a small typical dataset from giant data and has attracted a lot of attention from the deep learning community. Existing dataset distillation methods can be taxonomised into meta-learning and data match framework according to whether explicitly mimic target data. Albeit dataset distillation has shown a surprising performance in compressing datasets, it still possesses several limitations such as distilling high-resolution data. This paper provides a holistic understanding of dataset distillation from multiple aspects, including distillation frameworks and algorithms, disentangled dataset distillation, performance comparison, and applications. Finally, we discuss challenges and promising directions to further promote future studies about dataset distillation.