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Dataset Distillation Using Parameter Pruning

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences (IEICE Trans. Fundam.), 2022
Guang Li
Ren Togo
Takahiro Ogawa
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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