Papers › On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
Peng Sun, Bei Shi, Daiwei Yu, Tao Lin
Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands. Dataset distillation, as a recent emerging strategy, aims to compress real-world datasets for efficient training. However, this line of research currently struggle with large-scale and high-resolution datasets, hindering its practicality and feasibility. To this end, we re-examine the existing dataset distillation methods and identify three properties required for large-scale real-world applications, namely, realism, diversity, and efficiency. As a remedy, we propose RDED, a novel computationally-efficient yet effective data distillation paradigm, to enable both diversity and realism of the distilled data. Extensive empirical results over various neural architectures and datasets demonstrate the advancement of RDED: we can distill the full ImageNet-1K to a small dataset comprising 10 images per class within 7 minutes, achieving a notable 42% top-1 accuracy with ResNet-18 on a single RTX-4090 GPU (while the SOTA only achieves 21% but requires 6 hours).
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Code
Syntology Ran 3 of 10 code samples harvested from 2 repositories linked to this paper; 7 have no recorded run. Of those that ran: 2 ran · our draft was wrong; 1 ran · fixture could not drive it.
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10 samples harvested; 3 ran; 0 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.
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