IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508

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Dataset Distillation Using Parameter Pruning
Guang LIRen TOGOTakahiro OGAWAMiki HASEYAMA
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JOURNAL FREE ACCESS Advance online publication

Article ID: 2023EAL2053

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Abstract

In this study, we propose a novel dataset distillation method based on parameter pruning. The proposed method can synthesize more robust distilled datasets and improve distillation performance by pruning difficult-to-match parameters during the distillation process. Experimental results on two benchmark datasets show the superiority of the proposed method.

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