Dataset Distillation: Recent Advances of Methods and Challenges

01-10-2025
Original Article

Authors: Muyang Li; Yong Shi
Published in: Annals of Data Science

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Abstract

This article delves into the recent advancements and challenges in dataset distillation, a technique crucial for enhancing the efficiency of deep learning models. It explores various frameworks such as meta learning, parameter matching, distribution matching, and the integration of generative models. The article also highlights plug-and-play technologies that can improve the performance of dataset distillation algorithms. Additionally, it discusses the application of these methods across different data modalities, including text, time series, and video data. The article concludes by reflecting on the future directions and potential expansions of dataset distillation algorithms, emphasizing their importance in reducing data storage and training costs while maintaining model performance.

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Metadata
Literature

Title: Dataset Distillation: Recent Advances of Methods and Challenges
Authors: Muyang Li
Yong Shi
Publication date: 01-10-2025
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-025-00651-7

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