迁移学习算法研究

迁移学习算法研究迁移学习算法研究2016年4月18日TrainingDataClassifierUnseenData(…,long,T)good!Whatif…3传统监督机器学习(1/2)2024/5/11[fromProf.QiangYang]传统监督机器学习(2/2)42024/5/11传统监督学习同源、独立同分布两个基本假设标注足够多的训练样本在实际应用中通常不能满足！训练集测试集分类器训练集测试集分类器迁移学习52024/5/11实际应用学习场景HP新闻Lenovo新闻不同源、分布不一致人工标记训练样本，费时耗力迁移学习运用已有的知识对不同但相关领域问题进行求解的一种新的机器学习方法放宽了传统机器学习的两个基本假设迁移学习场景(1/4)62024/5/11迁移学习场景无处不在迁移知识迁移知识图像分类HP新闻Lenovo新闻新闻网页分类异构特征空间7Theappleisthepomaceousfruitoftheappletree,speciesMalusdomesticaintherosefamilyRosaceae...BananaisthecommonnameforatypeoffruitandalsotheherbaceousplantsofthegenusMusawhichproducethiscommonlyeatenfruit...Training:TextFuture:ImagesApplesBananas迁移学习场景(2/4)2024/5/11[fromProf.QiangYang]XinJin,FuzhenZhuang,SinnoJialinPan,ChangyingDu,PingLuo,QingHe:HeterogeneousMulti-taskSemanticFeatureLearningforClassification.CIKM2015:1847-1850.TestTestTrainingTrainingClassifierClassifier72.65%DVDElectronicsElectronics84.60%ElectronicsDrop!迁移学习场景(3/4)82024/5/11[fromProf.QiangYang]9DVDElectronicsBookKitchenClothesVideogameFruitHotelTeaImpractical!迁移学习场景(4/4)2024/5/11[fromProf.QiangYang]OutlineConceptLearningforTransferLearningConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningTransferLearningusingAuto-encodersTransferLearningfromMultipleSourceswithAutoencoderRegularizationSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders102024/5/11ConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningforTransferLearning112024/5/11IntroductionManytraditionallearningtechniquesworkwellonlyundertheassumption:Trainingandtestdatafollowthesamedistribution

Training(labeled)ClassifierTest(unlabeled)EnterpriseNewsClassification:includingtheclasses“ProductAnnouncement”,“Businessscandal”,“Acquisition”,……Productannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsDifferentdistributionFail!12ConceptLearningforTransferLearning2024/5/11Motivation(1/3)ExleAnalysis

Productannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsProductwordconceptLaserJet,printer,price,performanceThinkPad,ThinkCentre,price,performanceRelatedProductannouncementdocumentclass:13Sharesomecommonwords:announcement,price,performance…indicateConceptLearningforTransferLearning2024/5/11Motivation(2/3)ExleAnalysis:

HPLaserJet,printer,price,performanceetal.LenovoThinkpad,Thinkcentre,price,performanceetal.Thewordsexpressingthesamewordconceptaredomain-dependent

14ProductProductannouncementwordconceptindicatesTheassociationbetweenwordconceptsanddocumentclassesisdomain-independent

ConceptLearningforTransferLearning2024/5/11Motivation(3/3)15Furtherobservations:Differentdomainsmayusesamekeywordstoexpressthesameconcept(denotedasidenticalconcept)Differentdomainsmayalsousedifferentkeywordstoexpressthesameconcept(denotedasalikeconcept)Differentdomainsmayalsohavetheirowndistinctconcepts(denotedasdistinctconcept)TheidenticalandalikeconceptsareusedasthesharedconceptsforknowledgetransferWetrytomodelthesethreekindsofconceptssimultaneouslyfortransferlearningtextclassificationConceptLearningforTransferLearning2024/5/11PreliminaryKnowledgeBasicformulaofmatrixtri-factorization:wheretheinputXistheword-documentco-occurrencematrix

FGS16ConceptLearningforTransferLearning2024/5/11Previousmethod-MTrickinSDM2010(1/2)SketchmapofMTrick

SourcedomainXs

FsGsFtGtTargetdomainXtSKnowledgeTransfer17ConceptLearningforTransferLearning2024/5/11Consideringthealikeconcepts MTrick(2/2)OptimizationproblemforMTrickG0isthesupervisioninformationtheassociationSissharedasbridgetotransferknowledge18ConceptLearningforTransferLearningDualTransferLearning(Longetal.,SDM2012),consideringidenticalandalikeconcepts2024/5/11TriplexTransferLearning(TriTL)(1/5)Furtherdividethewordconceptsintothreekinds:

19F1,identicalconcepts;F2,alikeconcepts;F3,distinctconceptsInput:ssourcedomainXr(1≤r≤s)withlabelinformation,ttargetdomainXr(s+1≤r≤s+t)WeproposeTriplexTransferLearningframeworkbasedonmatrixtri-factorization(TriTLforshort)

2024/5/11ConceptLearningforTransferLearningF1,S1andS2

aresharedasthebridgeforknowledgetransferacrossdomainsThesupervisioninformationisintegratedbyGr(1≤r≤s)insourcedomainsTriTL(2/5)OptimizationProblem

202024/5/11ConceptLearningforTransferLearningTriTL(3/5)Wedevelopanalternativelyiterativealgorithmtoderivethesolutionandtheoreticallyanalyzeitsconvergence 212024/5/11ConceptLearningforTransferLearningTriTL(4/5)Classificationontargetdomains When1≤r≤s,Grcontainsthelabelinformation,soweremainitunchangedduringtheiterationswhenxibelongstoclassj,thenGr(i,j)=1,elseGr(i,j)=0Aftertheiteration,weobtaintheoutputGr(s+1≤r≤s+t),thenwecanperformclassificationaccordingtoGr222024/5/11ConceptLearningforTransferLearningTriTL(5/5)AnalysisofAlgorithmConvergence Accordingtothemethodologyofconvergenceanalysisinthetwoworks[Leeetal.,NIPS’01]and[Dingetal.,KDD’06],thefollowingtheoremholds.Theorem(Convergence):Aftereachroundofcalculatingtheiterativeformulas,theobjectivefunctionintheoptimizationproblemwillconvergemonotonically.232024/5/11ConceptLearningforTransferLearning242024/5/11rec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacecomp.graphicscomp.sys.ibm.pc.hardwarecomp.sys.mac.hardwarecomp.windows.xtalk.politics.misctalk.politics.gunstalk.politics.mideasttalk.religion.miscrecscicomptalkDataPreparation(1/3)20Newsgroups Fourtopcategories,eachtopcategorycontainsfoursub-categories SentimentClassification,fourdomains:books,dvd,electronics,kitchenRandomlyselecttwodomainsassources,andtherestastargets,then6problemscanbeconstructed

ConceptLearningforTransferLearning252024/5/11rec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacerec+sci-baseballcrypySourcedomainautosspaceTargetdomainFortheclassificationproblemwithonesourcedomainandonetargetdomain,wecanconstruct144()

problemsDataPreparation(2/3)Constructclassificationtasks(TraditionalTL)ConceptLearningforTransferLearning262024/5/11Constructnewtransferlearningproblemsrec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacerec+sci-baseballcrypyautosspacecomp.graphicscomp.sys.ibm.pc.hardwarecomp.sys.mac.hardwarecomp.windows.xtalk.politics.misctalk.politics.gunstalk.politics.mideasttalk.religion.misccomptalkautosgraphicsMoredistinctconceptsmayexist!DataPreparation(3/3)SourcedomainTargetdomainConceptLearningforTransferLearning272024/5/11ComparedAlgorithmsConceptLearningforTransferLearningTraditionallearningAlgorithmsSupervisedLearning:LogisticRegression(LR)[Davidetal.,00]SupportVectorMachine(SVM)[Joachims,ICML’99]Semi-supervisedLearning:TSVM[Joachims,ICML’99]TransferlearningMethods:CoCC[Daietal.,KDD’07],DTL[Longetal.,SDM’12]Classificationaccuracyisusedastheevaluationmeasure282024/5/11ExperimentalResults(1/3)ConceptLearningforTransferLearningSorttheproblemswiththeaccuracyofLRDegreeoftransferdifficultyeasierGenerally,thelowerofaccuracyofLRcanindicatethehardertotransfer,whilethehigheronesindicatetheeasiertotransferharder292024/5/11ExperimentalResults(2/3)ConceptLearningforTransferLearningComparisonsamongTriTL,DTL,MTrick,CoCC,TSVM,SVMandLRondatasetrecvs.sci(144problems)TriTLcanperformwelleventheaccuracyofLRislowerthan65%302024/5/11ExperimentalResults(3/3)ConceptLearningforTransferLearningResultsonnewtransferlearningproblems,weonlyselecttheproblems,whoseaccuraciesofLRarebetween(50%,55%](Onlyslightlybetterthanrandomclassification,thustheymightbemuchmoredifficult).Weobtain65problemsTriTLalsooutperformsallthebaselinesConclusionsExplicitlydefinethreekindsofwordconcepts,i.e.,identicalconcept,alikeconceptanddistinctconceptProposeageneraltransferlearningframeworkbasedonnonnegativematrixtri-factorization,whichsimultaneouslymodelthethreekindsofconcepts(TriTL)Extensiveexperimentsshowtheeffectivenessoftheproposedapproach,especiallywhenthedistinctconceptsmayexist312024/5/11ConceptLearningforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningConceptLearningforTransferLearning322024/5/11332024/5/11MotivationConceptLearningforTransferLearningProductannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsProductwordconceptLaserJet,printer,price,performanceThinkPad,ThinkCentre,price,performanceRelatedProductannouncementdocumentclass:Sharesomecommonwords:announcement,price,performance…indicateRetrospecttheexle

342024/5/11SomenotationsddocumentydocumentclasszwordconceptSomedefinitionse.g.,p(price|Product),p(LaserJet|Product,)wwordrdomaine.g,p(Product|Productannouncement)PreliminaryKnowledge(1/3)ConceptLearningforTransferLearning352024/5/11ConceptLearningforTransferLearningPreliminaryKnowledge(2/3)ProductLaserJet,printer,announcement,price,ThinkPad,ThinkCentre,announcement,priceProductannouncementp(w|z,r1)p(w|z,r2)p(z|y)p(w|z,r1)≠p(w|z,r2)E.g.,p(LaserJet|Product,HP)≠p(LaserJet|Product,Lenovo)p(z|y,r1)=p(z|y,r2)E.g.,p(Product|Productannoucement,HP)=p(Product|Productannoucement,Lenovo)Alikeconcept362024/5/11DualPLSA

(D-PLSA)Jointprobabilityoverallvariablesp(w,d)=p(w|z)p(z|y)p(d|y)p(y)GivendatadomainX,theproblemofmaximumloglikelihoodislogp(X;θ)=logΣz

p(Z,X;θ)

θ

includesalltheparametersp(w|z),p(z|y),p(d|y),p(y).Z

denotesallthelatentvariablesPreliminaryKnowledge(3/3)TheproposedtransferlearningalgorithmbasedonD-PLSA,denotedasHIDCConceptLearningforTransferLearning372024/5/11Identicalconceptp(w|za)p(za|y)AlikeconceptTheextensionandintensionaredomainindependentp(w|zb,r)p(zb|y)HIDC(1/3)Theextensionisdomaindependent,whiletheintensionisdomainindependentConceptLearningforTransferLearning382024/5/11Distinctconceptp(w|zc,r)p(zc|y,r)ThejointprobabilitiesofthesethreegraphicalmodelsHIDC(2/3)TheextensionandintensionarebothdomaindependentConceptLearningforTransferLearning392024/5/11Givens+t

datadomainsX={X1,…,Xs,Xs+1,…,Xs+t},withoutlossofgenerality,thefirstsdomainsaresourcedomains,andthelefttdomainsaretargetdomainsConsiderthethreekindsofconcepts:TheLog

likelihoodfunctionislogp(X;θ)=logΣz

p(Z,X;θ)

θ

includesallparametersp(w|za),p(w|zb,r),p(w|zc,r),p(za|y),p(zb|y),p(zc|y,r),p(d|y,r),p(y|r),p(r).HIDC(3/3)ConceptLearningforTransferLearning402024/5/11UsetheEMalgorithmtoderivethesolutionsEStep:ModelSolution(1/4)ConceptLearningforTransferLearning412024/5/11M

Step：ModelSolution(2/4)ConceptLearningforTransferLearning422024/5/11Semi-supervisedEMalgorithm:whenrisfromsourcedomains,thelabeledinformationp(d|y,r)isknownandp(y|r)

canbeinferedp(d|y,r)=1/ny,r,ifdbelongsyindomainr,ny,risthenumberofdocumentsinclassyindomainr,else

p(d|y,c)=0p(y|r)=ny,r/nr

,nr

isthenumberofdocumentsindomainr

whenrisfromsourcedomains,p(d|y,r)andp(y|r)keepunchangedduringtheiterations,whichsupervisetheoptimizingprocessModelSolution(3/4)ConceptLearningforTransferLearning432024/5/11ClassificationfortargetdomainsAfterweobtainthefinalsolutionsofp(w|za),p(w|zb,r),p(w|zc,r),p(za|y),p(zb|y),p(zc|y,r),p(d|y,r),p(y|r),p(r)Wecancomputetheconditionalprobabilities:

ThenthefinalpredictionisDuringtheiterations,alldomainssharep(w|za),p(za|y),p(zb|y),

whichactasthebridgeforknowledgetransferModelSolution(4/4)ConceptLearningforTransferLearning442024/5/11BaselinesComparedAlgorithmsSupervisedLearning:LogisticRegression(LG)[Davidetal.,00]SupportVectorMachine(SVM)[Joachims,ICML’99]Semi-supervisedLearning:TSVM[Joachims,ICML’99]TransferLearning:CoCC[Daietal.,KDD’07]CD-PLSA[Zhuangetal.,CIKM’10]DTL[Longetal.,SDM’12]OurMethodsHIDCMeasure:classificationaccuracyConceptLearningforTransferLearning452024/5/11Resultsonnewtransferlearningproblems,weselecttheproblems,whoseaccuraciesofLRarehigherthan50%,then334problemsareobtainedExperimentalResults(1/5)ConceptLearningforTransferLearning462024/5/11Resultsonnewtransferlearningproblems,weselecttheproblems,whoseaccuraciesofLRarehigherthan50%,then334problemsareobtainedExperimentalResults(2/5)ConceptLearningforTransferLearning472024/5/11ExperimentalResults(3/5)ConceptLearningforTransferLearning482024/5/11Sourcedomain:S

(rec.autos,

sci.space)，Targetdomain:T(,)STSTDistinctconceptSTAlikeconceptExperimentalResults(4/5)ConceptLearningforTransferLearning492024/5/11ExperimentalResults(5/5)ConceptLearningforTransferLearningIndeed,theproposedprobabilisticmethodHIDCisalsobetterthanTriTLThismayduetothereasonthatthereismoreclearerprobabilisticexplanationofHIDCp1(z,y)=p2(z,y)orp1(z|y)=p2(z|y)whichisbetter?p(z|y)p(y)502024/5/11[1]FuzhenZhuang,PingLuo,HuiXiong,QingHe,YuhongXiong,ZhongzhiShi:ExploitingAssociationsbetweenWordClustersandDocumentClassesforCross-DomainTextCategorization.SDM2010,pp.13-24.[2]FuzhenZhuang,PingLuo,ZhiyongShen,QingHe,YuhongXiong,ZhongzhiShi,HuiXiong:CollaborativeDual-PLSA:miningdistinctionandcommonalityacrossmultipledomainsfortextclassification.CIKM2010,pp.359-368.[3]FuzhenZhuang,PingLuo,ZhiyongShen,QingHe,YuhongXiong,ZhongzhiShi,HuiXiong:MiningDistinctionandCommonalityacrossMultipleDomainsUsingGenerativeModelforTextClassification.IEEETrans.Knowl.DataEng.24(11):2025-2039(2012).[3]FuzhenZhuang,PingLuo,ChangyingDu,QingHe,ZhongzhiShi:Triplextransferlearning:exploitingbothsharedanddistinctconceptsfortextclassification.WSDM2013,pp.425-434.[4]FuzhenZhuang,PingLuo,PeifengYin,QingHe,ZhongzhiShi.:ConceptLearningforCross-domainTextClassification:aGeneralProbabilisticFramework.IJCAI2013,pp.1960-1966.ReferencesConceptLearningforTransferLearningOutlineConceptLearningforTransferLearningConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningTransferLearningusingAuto-encodersTransferLearningfromMultipleSourceswithAutoencoderRegularizationSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders512024/5/11TransferLearningfromMultipleSourceswithAutoencoderRegularization522024/5/11TransferLearningUsingAuto-encoders53Motivation(1/2)TransferlearningbasedonoriginalfeaturespacemayfailtoachievehighperformanceonTargetdomaindataWeconsidertheautoencodertechniquetocollaborativelyfindanewrepresentationofbothsourceandtargetdomaindataElectronicsVideoGames

Compact;easytooperate;verygoodpicture,excited

aboutthequality;lookssharp!Averygood

game!Itisactionpacked

andfullofexcitement.Iamverymuchhooked

onthisgame.532024/5/11TransferLearningUsingAuto-encodersPreviousmethodsoftentransferfromonesourcedomaintoonetargetdomainWeconsidertheconsensusregularizedframeworkforlearningfrommultiplesourcedomainsDVDBookKitchenElectronicsWeproposeatransferlearningframeworkofconsensusregularizationautoencoderstolearnfrommultiplesourcesMotivation(2/2)542024/5/11TransferLearningUsingAuto-encodersAutoencoderNeuralNetworkMinimizingthereconstructionerrortoderivethesolution:whereh,garenonlinearactivationfunction,e.g.,Sigmoidfunction,forencodinganddecoding552024/5/11TransferLearningUsingAuto-encodersConsensusMeasure-(1/3)Exle:three-classclassificationproblem,threeclassifierspredictinstancesf1f2f3f1f2f3x1111x2333x3222x4231x5313x6123ConstraintSource1:D1Source2:D2Source3:D3562024/5/11TransferLearningUsingAuto-encodersConsensusMeasure-(2/3)Exle:three-classclassificationproblem,predictiononinstancexMinimalentropy,MaximalConsensusMaximalentropy,MinimalConsensusEntropybasedConsensusMeasure(Luoetal.,CIKM’08)θiistheparametervectorofclassifieri,Cistheclasslabelset572024/5/11TransferLearningUsingAuto-encodersConsensusMeasure-(3/3)Forsimplicity,theconsensusmeasureforbinaryclassificationcanberewrittenasInthiswork,weimposetheconsensusregularizationtoautoencoders,andtrytoimprovethelearningperformancefrommultiplesourcedomainssincetheireffectsonmakingthepredictionconsensusaresimilar.582024/5/11TransferLearningUsingAuto-encodersSomeNotationsSourcedomainsGivenrsourcedomains:,i.e.,

,.ThefirstcorrespondingdatamatrixisTargetdomainThecorrespondingdatamatrixisThegoalistotrainaclassifier

ftomakeprecisepredictionson.592024/5/11TransferLearningUsingAuto-encodersFrameworkofCRAThedatafromallsourceandtargetdomainssharethesameencodinganddecodingweightsTheclassifierstrainedfromthenewrepresentationareregularizedtopredictthesameresultsontargetdomaindata602024/5/11TransferLearningUsingAuto-encodersOptimizationProblemofCRATheoptimizationproblem:ReconstructionError612024/5/11TransferLearningUsingAuto-encodersOptimizationProblemofCRATheoptimizationproblem:ConsensusRegularization622024/5/11TransferLearningUsingAuto-encodersOptimizationProblemofCRATheoptimizationproblem:ThetotallossofsourceclassifiersoverthecorrespondingsourcedomaindatawiththehiddenrepresentationWeighdecayterm632024/5/11TransferLearningUsingAuto-encodersTheSolutionofCRAWeusethegradientdescentmethodtoderivethesolutionofallparametersƞisthelearningrate.ThetimecomplexityisO(rnmk)Theoutput:theencodinganddecodingparameters,andsourceclassifierswithlatentrepresentation.642024/5/11TransferLearningUsingAuto-encodersTargetClassifierConstructionTwoScheme:Trainthesourceclassifiersbasedonandcombinethemas,whereCombineallthesourcedomaindataasZSandtrainaunifiedclassifierusinganysupervisedlearningalgorithms,e.g.,SVM,LogisticRegression(LR).ThetwoaccuraciesaredenotedasCRAvandCRAu,respectively652024/5/11TransferLearningUsingAuto-encodersDataSets-(1/2)ImageData(fromLuoetal.,CIKM08)(Someexles)AB

A1A2A3A4B1B2B3B4Threesources:A1B1A2B2A3B3Targetdomain:A4B4Totally,96()3-sourcevs1-targetdomain(3vs1)probleminstancescanbeconstructedfortheexperimentalevaluation662024/5/11TransferLearningUsingAuto-encodersDataSets-(2/2)SentimentClassification(fromBlitzeretal.,ACL07)Four3-sourcevs1-targetdomainclassificationproblemsareconstructedDVDBookKitchenElectronicsTheaccuracyontargetdomaindataisusedastheevaluationmeasureBothSVMandLRareusedtotrainclassifiersonthenewrepresentation672024/5/11TransferLearningUsingAuto-encodersAllComparedAlgorithmsBaselinesSupervisedlearningonoriginalfeatures:SVM[Joachims,ICML’99],LogisticRegression(LR)[Davidetal.,00]Embeddingmethodbasedonautoencoders(EAER)[Yuetal.,ECML’13]MarginalizedStackedDenoisingAutoencoders

(mSDA)[Chenetal.,ICML’12]TransferComponentAnalysis(TCA)[Panetal.,TNN’11]Transferlearningfrommultiplesources(CCR3)(Luoetal.,CIKM’08)Ourmethod:CRAvandCRAuForthemethodswhichcannothandlemultiplesources,wetraintheclassifiersfromeachsourcedomainandmergeddataofallsources(r+1accuracies).Finally,maximal,meanandminimalvaluesarereported.682024/5/11TransferLearningUsingAuto-encoders69ExperimentalResults-(1/2)TransferLearningwithMultipleSourcesviaConsensusRegularizationAutoencodersFuzhenZhuang,XiaohuCheng,SinnoJialinPan,WenchaoYu,QingHe,andZhongzhiShiResultson96imageclassificationproblems70ExperimentalResults-(2/2)TransferLearningwithMultipleSourcesviaConsensusRegularizationAutoencodersFuzhenZhuang,XiaohuCheng,SinnoJialinPan,WenchaoYu,QingHe,andZhongzhiShiResultson4sentimentclassificationproblemsConclusionsThewellknownrepresentationlearningtechniqueautoencoderisconsidered,andweformalizetheautoencodersandconsensusregularizationintoaunifiedoptimizationframeworkExtensivecomparisonexperimentsonimageandsentimentdataareconductedtoshowtheeffectivenessoftheproposealgorithm712024/5/11TransferLearningUsingAuto-encodersSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders722024/5/11TransferLearningUsingAuto-encodersAutoencoderisanunsupervisedfeaturelearningalgorithm,whichcannoteffectivelymakeuseofthelabelinformationLimitationofBasicAutoencoderContributionofThisWorkWeextendAutoencodertomulti-layerstructure,andincorporatethelabelasonelayerMotivation732024/5/11TransferLearningUsingAuto-encoders源领域和目标领域共享编码和解码权重利用KL距离对隐层空间进行约束利用多类回归模型对类标层进行约束FrameworkofTLDA(1/5)742024/5/11TransferLearningUsingAuto-encoders目标是最小化重构误差：DeepAutoencoderFrameworkofTLDA(2/5)752024/5/11TransferLearningUsingAuto-encodersKL距离KL距离衡量的是两个概率分布的差异情况，计算公式如下：以上