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1、DeepLambertianNetworksYichuanTangtang@cs.toronto.eduRuslanSalakhutdinovrsalakhu@cs.toronto.eduGeoffreyHintonhinton@cs.toronto.eduDepartmentofComputerScience,UniversityofToronto,Toronto,Ontario,CANADAAbstractdirectionandintensitychangesinascene,dramaticchangesinimageintensityoccur
2、.Thisisdetrimen-Visualperceptionisachallengingproblemintaltorecognitionperformanceasmostalgorithmsusepartduetoilluminationvariations.Apos-imageintensitiesasinputs.Anaturalwayofattack-siblesolutionistofirstestimateanillumi-ingthisproblemistolearnamodelwherethealbedo,nationinvarian
3、trepresentationbeforeusingsurfacenormals,andthelightingareexplicitlyrepre-itforrecognition.Theobjectalbedoandsentedasthelatentvariables.Sincethealbedoandsurfacenormalsareexamplesofsuchrep-surfacenormalsarephysicalpropertiesofanobject,resentations.Inthispaper,weintroduceatheyaref
4、eatureswhichareinvariantw.r.t.illumina-multilayergenerativemodelwherethelatenttion.variablesincludethealbedo,surfacenormals,andthelightsource.CombiningDeepBe-SeparatingthesurfacenormalsandthealbedoofliefNetswiththeLambertianreflectanceas-objectsusingmultipleimagesobtainedunderdif
5、-sumption,ourmodelcanlearngoodpriorsferentlightingconditionsisknownasphotometricoverthealbedofrom2Dimages.Illumina-stereo(Woodham,1980).Hayakawa(1994)describedtionvariationscanbeexplainedbychangingamethodforphotometricstereousingSVD,whichonlythelightinglatentvariableinourmodel.e
6、stimatedtheshapeandalbedouptoalineartrans-Bytransferringlearnedknowledgefromsim-formation.Usingintegrabilityconstraints,Yuilleetal.ilarobjects,albedoandsurfacenormalses-(1999)proposedasimilarmethodtoreducetheambi-timationfromasingleimageispossibleinguitiestoageneralizedbasrelief
7、ambiguity.Arelatedourmodel.Experimentsdemonstratethatproblemistheestimationofintrinsicimages(Barrowourmodelisabletogeneralizeaswellasim-&Tenenbaum,1978;Gehleretal.,2011).However,inproveoverstandardbaselinesinone-shotfacethoseworks,theshading(innerproductofthelight-recognition.in
8、gvectorandthesurfacenormalvector)insteadofthesu
