Evgeny A. Smirnov, Denis M. Timoshenko, Serge N. Andrianov
{"title":"ImageNet分类的正则化方法与深度卷积神经网络的比较","authors":"Evgeny A. Smirnov, Denis M. Timoshenko, Serge N. Andrianov","doi":"10.1016/j.aasri.2014.05.013","DOIUrl":null,"url":null,"abstract":"<div><p>Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.</p></div>","PeriodicalId":100008,"journal":{"name":"AASRI Procedia","volume":"6 ","pages":"Pages 89-94"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aasri.2014.05.013","citationCount":"166","resultStr":"{\"title\":\"Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks\",\"authors\":\"Evgeny A. Smirnov, Denis M. Timoshenko, Serge N. Andrianov\",\"doi\":\"10.1016/j.aasri.2014.05.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.</p></div>\",\"PeriodicalId\":100008,\"journal\":{\"name\":\"AASRI Procedia\",\"volume\":\"6 \",\"pages\":\"Pages 89-94\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.aasri.2014.05.013\",\"citationCount\":\"166\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AASRI Procedia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212671614000146\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AASRI Procedia","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212671614000146","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks
Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.
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