台大-李宏毅-B站机器学习视频-课件神经网络与深度学习VAE v5

UnsupervisedLearning:Generation,Creation,GenerativeModels:,WhatIcannotcreate,Idonotunderstand.,RichardFeynman,CreationImageProcessing,Now,Inthefuture,v.s.,Machinedrawsacat,GenerativeModels,PixelRNN,Tocreateanimage,generatingapixeleachtime,Ref:AaronvandenOord,NalKalchbrenner,KorayKavukcuoglu,PixelRecurrentNeuralNetworks,arXivpreprint,2016,E.g.3x3images,NN,NN,NN,Canbetrainedjustwithalargecollectionofimageswithoutanyannotation,PixelRNN,RealWorld,Ref:AaronvandenOord,NalKalchbrenner,KorayKavukcuoglu,PixelRecurrentNeuralNetworks,arXivpreprint,2016,Morethanimages,Audio:AaronvandenOord,SanderDieleman,HeigaZen,KarenSimonyan,OriolVinyals,AlexGraves,NalKalchbrenner,AndrewSenior,KorayKavukcuoglu,WaveNet:AGenerativeModelforRawAudio,arXivpreprint,2016,Video:NalKalchbrenner,AaronvandenOord,KarenSimonyan,IvoDanihelka,OriolVinyals,AlexGraves,KorayKavukcuoglu,VideoPixelNetworks,arXivpreprint,2016,PracticingGenerationModels:PokmonCreation,Smallimagesof792PokmonsCanmachinelearntocreatenewPokmons?Sourceofimage:,Dontcatchthem!Createthem!,Originalimageis40 x40,Makingtheminto20 x20,PracticingGenerationModels:PokmonCreation,Tips(?),R=50,G=150,B=100,Eachpixelisrepresentedby3numbers(correspondingtoRGB),Eachpixelisrepresentedbya1-of-Nencodingfeature,Clusteringthesimilarcolor,167colorsintotal,PracticingGenerationModels:PokmonCreation,Originalimage(40 x40):.tw/tlkagk/courses/ML_2016/Pokemon_creation/image.rarPixels(20 x20):.tw/tlkagk/courses/ML_2016/Pokemon_creation/pixel_color.txtEachlinecorrespondstoanimage,andeachnumbercorrespondstoapixel.tw/tlkagk/courses/ML_2016/Pokemon_creation/colormap.txtFollowingexperiment:1-layerLSTM,512cells,0,1,2,RealPokmon,Cover50%,Cover75%,Itisdifficulttoevaluategeneration.,Neverseenbymachine!,PokmonCreation,Drawingfromscratch,Needsomerandomness,GenerativeModels,DiederikPKingma,MaxWelling,Auto-EncodingVariationalBayes,arXivpreprint,2013,Auto-encoder,Ascloseaspossible,NNEncoder,NNDecoder,code,NNDecoder,code,Randomlygenerateavectorascode,Image?,NNEncoder,NNDecoder,code,input,output,Auto-encoder,VAE,NNEncoder,input,NNDecoder,output,2,3,Fromanormaldistribution,3,1,2,X,+,Minimizereconstructionerror,=131+2,exp,=+,Minimize,Cifar-10,Sourceofimage:/pdf/1606.04934v1.pdf,NNEncoder,input,NNDecoder,output,2,3,3,1,2,X,+,exp,PokmonCreation,Training,NNDecoder,3,1,2,10-dim,10-dim,Picktwodim,andfixtheresteight,?,Ref:http:/www.wired.co.uk/article/google-artificial-intelligence-poetrySamuelR.Bowman,LukeVilnis,OriolVinyals,AndrewM.Dai,RafalJozefowicz,SamyBengio,GeneratingSentencesfromaContinuousSpace,arXivprepring,2015,WritingPoetry,NNEncoder,sentence,NNDecoder,sentence,code,CodeSpace,iwenttothestoretobuysomegroceries.,comewithme,shesaid.,istoretobuysomegroceries.,iweretobuyanygroceries.,talktome,shesaid.,dontworryaboutit,shesaid.,WhyVAE?,?,encode,decode,code,IntuitiveReason,noise,noise,WhyVAE?,IntuitiveReason,NNEncoder,input,NNDecoder,output,2,3,3,1,2,X,+,exp,OriginalCode,Codewithnoise,Thevarianceofnoiseisautomaticallylearned,Whatwillhappenifweonlyminimizereconstructionerror?,=131+2,Minimize,WhyVAE?,IntuitiveReason,NNEncoder,input,NNDecoder,output,2,3,3,1,2,X,+,exp,OriginalCode,Codewithnoisy,Thevarianceofnoiseisautomaticallylearned,Whatwillhappenifweonlyminimizereconstructionerror?,=131+2,Minimize,Wewantcloseto0(variancecloseto1),L2regularization,WhyVAE?,Backtowhatwewanttodo,Estimatetheprobabilitydistribution,EachPokmonisapointxinthespace,P(x),P(x),=|,P(m),1,2,3,4,5,.,GaussianMixtureModel,Howtosample?,|,Eachxyougenerateisfromamixture,Distributedrepresentationisbetterthancluster.,(multinomial),misaninteger,P(x),z,VAE,0,|,zisavectorfromnormaldistribution,Eachdimensionofzrepresentsanattribute,InfiniteGaussian,Eventhoughzisfrom0,P(x)canbeverycomplex,=|,P(z)isnormaldistribution,MaximizingLikelihood,isgoingtobeestimated,TuningtheparameterstomaximizelikelihoodL,Weneedanotherdistributionq(z|x),Decoder,Encoder,NN,=|,|,|,=,Maximizingthelikelihoodoftheobservedx,=|,q(z|x)canbeanydistribution,=|,|,=|,|,=|,|+|,|,|,MaximizingLikelihood,0,=,P(z)isnormaldistribution,Maximizingthelikelihoodoftheobservedx,isgoingtobeestimated,=|,|,MaximizingLikelihood,=+|,=|,Maximizeby|,Find|and|maximizingLb,|willbeanapproximationof|intheend,=|,|,=|,=|,+|,|,MaximizingLikelihood,NN,P(z)isnormaldistribution,Maximizingthelikelihoodoftheobservedx,isgoingtobeestimated,=|,|,=,|,ConnectionwithNetwork,|,Minimizing,=131+2,Minimize,|,(RefertotheAppendixBoftheoriginalVAEpaper),=|,Maximizing,NN,NN,NN,close,Thisistheauto-encoder,ConditionalVAE,/pdf/1406.5298v2.pdf,Tolearnmore,CarlDoersch,TutorialonVariationalAutoencodersDiederikP.Kingma,DaniloJ.Rezende,ShakirMohamed,MaxWelling,“Semi-supervisedlearningwithdeepgenerativemodels.”NIPS,2014.Sohn,Kihyuk,HonglakLee,andXinchenYan,“LearningStructuredOutputRepresentationusingDeepConditionalGenerativeModels.”NIPS,2015.XinchenYan,JimeiYang,KihyukSohn,HonglakLee,“Attribute2Image:ConditionalImageGenerationfromVisualAttributes”,ECCV,2016Cooldemo:http:/vdumoulin.github.io/morphing_faces/http:/fvae.ail.tokyo/,ProblemsofVAE,Itdoesnotreallytrytosimulaterealimages,NNDecoder,code,Output,Onepixeldifferencefromthetarget,Onepixeldifferencefromthetarget,Realistic,Fake,VAEmayjustmemorizetheexistingimages,insteadofgeneratingnewimages,GenerativeModels,IanJ.Goodfellow,JeanPouget-Abadie,MehdiMirza,BingXu,DavidWarde-Farley,SherjilOzair,AaronCourville,YoshuaBengio,GenerativeAdversarialNetworks,arXivpreprint2014,YannLeCunscomment,YannLeCunscomment,擬態的演化,棕色,葉脈,蝴蝶不是棕色,蝴蝶沒有葉脈,.,Theevolutionofgeneration,NNGeneratorv1,Discri-minatorv1,Realimages:,NNGeneratorv2,Discri-minatorv2,NNGeneratorv3,Discri-minatorv3,GAN-Discriminator,NNGeneratorv1,Realimages:,Discri-minatorv1,image,1/0,(realorfake),DecoderinVAE,Vectorsfromadistribution,1,1,1,1,0,0,0,0,GAN-Generator,Discri-minatorv1,NNGeneratorv1,Randomlysampleavector,0.87,“Tuning”theparametersofgenerator,Theoutputbeclassifiedas“real”(ascloseto1aspossible),Generator+Discriminator=anetwork,Usinggradientdescenttofindtheparametersofgenerator,Fixthediscriminator,1.0,GANToyExample,Demo:/people/karpathy/gan/,Discri-minator,NNGenerator,x,z,1/0,Greendistribution,Bluecurve,Realdata(blackpoints),Cifar-10,Whichoneismachine-generated?,Ref:,Movingonthecodespace,AlecRadford,LukeMetz,SoumithChintala,UnsupervisedRepresentationLearningwithDeepConvolutionalGenerativeAdversarialNetworks,ICLR,2016,畫漫畫,Ref:,畫漫畫,Ref:,Webdemo:http:/mattya.github.io/chainer-DCGAN/,Inpractical,GANsaredifficulttooptimize