基于对比约束的可解释小样本学习-豆柴文库

您所在位置：网站首页 / 基于对比约束的可解释小样本学习.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

基于对比约束的可解释小样本学习
Title:Contrastive-basedExplanationsforSmall-SampleLearning
Abstract:
Small-samplelearningreferstothescenariowheretherearelimitedlabeledexamplesavailablefortrainingamachinelearningmodel.Thisposesasignificantchallengeastraditionalapproachesstruggletoachievesatisfactoryperformancewithsuchlimiteddata.Inthispaper,weproposeanovelapproachcalledcontrastive-basedexplanationsforsmall-samplelearning.Throughcontrastivelearning,weaimtoleveragebothlabeledandunlabeleddatatoenhancetheperformanceandinterpretabilityofmodelsinsmall-samplelearningtasks.Wedemonstratetheeffectivenessofourapproachonvariousexperimentalsetupsandprovideinsightsintotheinterpretabilityofthetrainedmodels.
1.Introduction:
Inrecentyears,thepopularityofmachinelearninghasgrownexponentially,andwithit,thedemandfortrainingmodelsonlimitedlabeledexamples.Small-samplelearning,wheretheavailabilityoflabeleddataissignificantlyrestricted,isacommonscenarioinvariousdomainssuchashealthcare,finance,andnaturallanguageprocessing.Traditionalapproachesoftenfailtoachievedesirableaccuracywithlimiteddata,highlightingtheneedforalternativetechniques.
2.ContrastiveLearning:
Contrastivelearningisaself-supervisedlearningtechniquethatleveragesunlabeleddatatolearnusefulrepresentationsbycomparinginstancesagainsteachother.Bymaximizingthesimilaritybetweeninstancesofthesameclassandminimizingitbetweeninstancesofdifferentclasses,contrastivelearningcangeneraterobustrepresentationsthatcaptureimportantdiscriminativeinformation.Weapplycontrastivelearningtosmall-samplelearningbyexploitingunlabeleddatatoenhancethemodel'sunderstandingoftheunderlyingdatadistribution.
3.ModelArchitecture:
Weproposeacontrastive-basedexplanationmodelthatconsistsoftwokeycomponents:afeatureencoderandacontrastivelossfunction.Thefeatureencodertransformstheinputdataintoalatentrepresentationspace,wherethecontrastivelossfunctionmeasuresthesimilaritybetweeninstances.Bytrainingthemodeltodistinguishbetweensimilaranddissimilarinstances,itlearnstocapturecrucialdiscriminat