Dataset Distillation Using Parameter Pruning

• URL: http://arxiv.org/abs/2209.14609v6
• Date: Mon, 21 Aug 2023 03:15:35 GMT
• Title: Dataset Distillation Using Parameter Pruning
• Authors: Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama
• Abstract summary: 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.
• Score: 53.79746115426363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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