Anthony Reiling, William Mitchell, Stefan Westberg, E. Balster, T. Taha
{"title":"基于遗传算法的CNN优化","authors":"Anthony Reiling, William Mitchell, Stefan Westberg, E. Balster, T. Taha","doi":"10.1109/NAECON46414.2019.9058307","DOIUrl":null,"url":null,"abstract":"Hand tuning convolutional neural networks (CNN) for performance optimization can be tedious. A novel approach using a genetic algorithm to automate CNN hyper-parameter adjustment is proposed. This automated approach shows a 5% accuracy improvement over hand tuned methods and highly energy efficient networks on the Intel Movidius Compute Stick.","PeriodicalId":193529,"journal":{"name":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"CNN Optimization with a Genetic Algorithm\",\"authors\":\"Anthony Reiling, William Mitchell, Stefan Westberg, E. Balster, T. Taha\",\"doi\":\"10.1109/NAECON46414.2019.9058307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hand tuning convolutional neural networks (CNN) for performance optimization can be tedious. A novel approach using a genetic algorithm to automate CNN hyper-parameter adjustment is proposed. This automated approach shows a 5% accuracy improvement over hand tuned methods and highly energy efficient networks on the Intel Movidius Compute Stick.\",\"PeriodicalId\":193529,\"journal\":{\"name\":\"2019 IEEE National Aerospace and Electronics Conference (NAECON)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE National Aerospace and Electronics Conference (NAECON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAECON46414.2019.9058307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE National Aerospace and Electronics Conference (NAECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON46414.2019.9058307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hand tuning convolutional neural networks (CNN) for performance optimization can be tedious. A novel approach using a genetic algorithm to automate CNN hyper-parameter adjustment is proposed. This automated approach shows a 5% accuracy improvement over hand tuned methods and highly energy efficient networks on the Intel Movidius Compute Stick.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
