{"title":"假标签平滑","authors":"Ziyang Luo, Yadong Xi, Xiaoxi Mao","doi":"10.1145/3459637.3482184","DOIUrl":null,"url":null,"abstract":"Label Smoothing is a widely used technique in many areas. It can prevent the network from being over-confident. However, it hypotheses that the prior distribution of all classes is uniform. Here, we decide to abandon this hypothesis and propose a new smoothing method, called Smoothing with Fake Label. It shares a part of the prediction probability to a new fake class. Our experiment results show that the method can increase the performance of the models on most tasks and outperform the Label Smoothing on text classification and cross-lingual transfer tasks.","PeriodicalId":405296,"journal":{"name":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","volume":"313 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smoothing with Fake Label\",\"authors\":\"Ziyang Luo, Yadong Xi, Xiaoxi Mao\",\"doi\":\"10.1145/3459637.3482184\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Label Smoothing is a widely used technique in many areas. It can prevent the network from being over-confident. However, it hypotheses that the prior distribution of all classes is uniform. Here, we decide to abandon this hypothesis and propose a new smoothing method, called Smoothing with Fake Label. It shares a part of the prediction probability to a new fake class. Our experiment results show that the method can increase the performance of the models on most tasks and outperform the Label Smoothing on text classification and cross-lingual transfer tasks.\",\"PeriodicalId\":405296,\"journal\":{\"name\":\"Proceedings of the 30th ACM International Conference on Information & Knowledge Management\",\"volume\":\"313 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 30th ACM International Conference on Information & Knowledge Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3459637.3482184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3459637.3482184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Label Smoothing is a widely used technique in many areas. It can prevent the network from being over-confident. However, it hypotheses that the prior distribution of all classes is uniform. Here, we decide to abandon this hypothesis and propose a new smoothing method, called Smoothing with Fake Label. It shares a part of the prediction probability to a new fake class. Our experiment results show that the method can increase the performance of the models on most tasks and outperform the Label Smoothing on text classification and cross-lingual transfer tasks.
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