{"title":"光网络中QoT估计的域自适应方法评价","authors":"R. Marino, C. Rottondi, A. Giusti, A. Bianco","doi":"10.1364/ofc.2020.th3d.2","DOIUrl":null,"url":null,"abstract":"We evaluate the performance of two domain adaptation approaches for machine learning assisted quality of transmission estimation of an optical lightpath, for a fixed/variable number of available training samples from the source/target domain.","PeriodicalId":173355,"journal":{"name":"2020 Optical Fiber Communications Conference and Exhibition (OFC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Assessment of Domain Adaptation Approaches for QoT Estimation in Optical Networks\",\"authors\":\"R. Marino, C. Rottondi, A. Giusti, A. Bianco\",\"doi\":\"10.1364/ofc.2020.th3d.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We evaluate the performance of two domain adaptation approaches for machine learning assisted quality of transmission estimation of an optical lightpath, for a fixed/variable number of available training samples from the source/target domain.\",\"PeriodicalId\":173355,\"journal\":{\"name\":\"2020 Optical Fiber Communications Conference and Exhibition (OFC)\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Optical Fiber Communications Conference and Exhibition (OFC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/ofc.2020.th3d.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Optical Fiber Communications Conference and Exhibition (OFC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/ofc.2020.th3d.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessment of Domain Adaptation Approaches for QoT Estimation in Optical Networks
We evaluate the performance of two domain adaptation approaches for machine learning assisted quality of transmission estimation of an optical lightpath, for a fixed/variable number of available training samples from the source/target domain.
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