{"title":"Dataset Distillation Using Parameter Pruning","authors":"Guang LI, Ren TOGO, Takahiro OGAWA, Miki HASEYAMA","doi":"10.1587/transfun.2023eal2053","DOIUrl":null,"url":null,"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.","PeriodicalId":55003,"journal":{"name":"Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1587/transfun.2023eal2053","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 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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