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1、2014/12/10受大脑启发的层次化视觉计算模型Brain-inspiredhierarchicalvisualcomputingmodelsXiaolinHu（胡晓林）DepartmentofComputerScience&TechnologyTsinghuaUniversityBackgroundHowcancomputerEU:HumanBrainProjectscientistsbeinvolved?•Understandthebrainwithcomputersciencetechniques•Developmoreefficientcompu
2、tersciencetechniquesbasedonbrainsciencefindingsUSA:BRAINinitiative2Goalsofbrain-inspiredmodelsWhat‘sagoodcomputationalmodel?•Achieveeithergoal•AchievebothgoalsInventmoreUnderstandefficientvisualprocessingcomputervisionmechanismsofalgorithmsthebrain3TypesofmodelsThistalkBiologicall
3、yBiologicallyArtificialdetailedfeasiblemodelsmodelsmodels4Visualsystem5Beyondprimaryvisualcortex背侧通路Where腹侧通路What6Outline•OuterproductmodelforV1cells•HierarchicalK-meansmodelforV2cells•SparseHMAXmodelforventralpathway•Hierarchicalmodelforsaliencyprediction•Reversehierarchymodelfor
4、saliencyprediction7TwotypesofV1cellsComplexcellSimplecell8SparsecodingforV1simplecellsSparsecoding:EachinputxisaweightedsumofafewbasesajHiddensVisiblexRecon.errorsparsityActivitiesofV1cellsofawakerats(Greenberg,etal.,2008)9Olshausen,Field,Nature,199610ApplicationsYang,etal.,PAMI20
5、09Yang,etal.,CVPR’0911CovariancemodelforV1complexcells•AssumeaGaussiandistributionforimageintensities–Basesareincorporatedintocovariance•Disadvantage–Unstable–NotbiologicallyplausibleKarklin,Lewicki,Nature,200912OuterproductmodelforV1cells•Assumetheouterproductofimageintensitiesob
6、eysaGaussiandistribution•wherethehiddenvariableissparse•Bymaximizingthedatalog-likelihoodwehave13Qi,Hu,NeuralComputation,2014Themodel14FirstlayerresultsresemblepropertiesofV1simplecells15SecondlayerresultsresemblepropertiesofV1complexcells16AdvantagesoversparsecodingPixelintensiti
7、es2ndlayer1stlayerSparsecoding17ApplicationtoimageclassificationPerformanceon15-scenebenchmarkQi,Su,Hu,ICACI’1318Outline•OuterproductmodelforV1cells•HierarchicalK-meansmodelforV2cells•SparseHMAXmodelforventralpathway•Hierarchicalmodelforsaliencyprediction•Reversehierarchymodelfors
8、aliencyprediction19ResponsesofV2c
