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1、ExploitingStructureinCrowdsourcingTasksviaLatentFactorModelsPaulRuvolo,JacobWhitehill,JavierR.MovellanMachinePerceptionLaboratoryUCSanDiego,CAAbstractInternetcrowdsourcingservicessuchastheAmazonMechanicalTurk(1)andtheESPGame(15)havebecomeimportanttoolsforthemachinelear
2、ningcommu-nitybyfacilitatingthedistributedlabelingoflargedatasetsatlittlecost.Akeychallengewhenusingcrowdsourcingtolabeldatabasesistheneedtoderivehighqualitylabelsbyaggregatingtheresponsesfromlabelersofvaryingreliabilityoverdatainstancesofvaryingdifficulty.Existingalgor
3、ithmsforqualitycontrolandlabelinference(14;17;10)sufferseveralsignificantshortcomings:(1)Exist-ingmethodsareincapableofmodelinginteractioneffectsbetweenlabeleranddataitems,suchaswhensomelabelershavespecializedknowledgeaboutaparticularsubsetofitems.(2)Existingalgorithmsa
4、ssumethatlabelers’accuracies,aswellasdatainstances’difficulties,areindependent.Inreality,theremaybeaprioriin-formationaboutlabelers(ordatainstances)thatpredictsthoselabelers’accuracyatthelabelingtask.Analogously,certainfeaturessharedamongdatainstancesmaypredicttheirdiffi
5、cultyofbeinglabeledcorrectly.Inthispaper,wepresentanalgorithmthataddressesbothoftheseshortcomings.Wedemonstratethattheproposedalgorithmdeliverssuperioraccuracy,comparedtopreviousmethods,ofinferringdatalabelsonadifficultfacialexpressionlabelingtask.Finally,weshowthatourp
6、roposedmodelsubsumescertainpreviousmodelsasspecialcases.1IntroductionAsmachinelearningapplicationstacklemoreandmoredifficultproblems,theimportanceoflarge-scaleandvarieddatasetsofhigh-qualitytrainingdatabecomesmoreapparent.Forinstance,theOmronfacedetector,whichrepresents
7、thecurrentstateoftheartoffacedetection,wastrainedusingmillionsofhand-labeledimages(9).Inordertomeetthelargedemandforlabeleddata,researchershavebeenincreasinglyrelyingoncrowdsourcingservicesthatallowthemtoharnessvastpoolsofhumanlabelersatverylowcost.Currentsystemsinclud
8、ePay-per-labelservicessuchasAmazon’sMechanicalTurk(1)andinteractivegamessuchasHerdIt(2)andtheESPgame(15).Ascrowdsourc