GAN (v11)Special Structure (v6)李宏毅深度学习.ppt

GenerativeAdversarialNetwork GAN RestrictedBoltzmannMachine http speech ee ntu edu tw tlkagk courses MLDS 2015 2 Lecture RBM 20 v2 ecm mp4 index htmlGibbsSampling http speech ee ntu edu tw tlkagk courses MLDS 2015 2 Lecture MRF 20 v2 ecm mp4 index html Outlook NIPS2016Tutorial GenerativeAdversarialNetworks Author IanGoodfellowPaper https arxiv org abs 1701 00160Video YoucanfindtipsfortrainingGANhere Review Generation Drawing WritingPoems Review Auto encoder Ascloseaspossible NNEncoder NNDecoder code NNDecoder code Randomlygenerateavectorascode Image Review Auto encoder 2D 1 5 1 5 1 50 NNDecoder 1 50 NNDecoder Review Auto encoder 1 5 1 5 NNEncoder NNDecoder code input output Auto encoder VAE NNEncoder input NNDecoder output 2 3 Fromanormaldistribution 3 1 2 X Minimizereconstructionerror 13 1 2 exp Minimize Auto EncodingVariationalBayes https arxiv org abs 1312 6114 ProblemsofVAE Itdoesnotreallytrytosimulaterealimages NNDecoder code Output Onepixeldifferencefromthetarget Onepixeldifferencefromthetarget Realistic Fake Theevolutionofgeneration NNGeneratorv1 Discri minatorv1 Realimages NNGeneratorv2 Discri minatorv2 NNGeneratorv3 Discri minatorv3 BinaryClassifier Theevolutionofgeneration NNGeneratorv1 Discri minatorv1 Realimages NNGeneratorv2 Discri minatorv2 NNGeneratorv3 Discri minatorv3 GAN Discriminator NNGeneratorv1 Realimages Discri minatorv1 image 1 0 realorfake SomethinglikeDecoderinVAE Randomlysampleavector 1 1 1 1 0 0 0 0 GAN Generator Discri minatorv1 NNGeneratorv1 Randomlysampleavector 0 13 Updatingtheparametersofgenerator Theoutputbeclassifiedas real ascloseto1aspossible Generator Discriminator anetwork Usinggradientdescenttoupdatetheparametersinthegenerator butfixthediscriminator 1 0 v2 GAN 二次元人物頭像鍊成 Sourceofimages DCGAN GAN 二次元人物頭像鍊成 100rounds GAN 二次元人物頭像鍊成 1000rounds GAN 二次元人物頭像鍊成 2000rounds GAN 二次元人物頭像鍊成 5000rounds GAN 二次元人物頭像鍊成 10 000rounds GAN 二次元人物頭像鍊成 20 000rounds GAN 二次元人物頭像鍊成 50 000rounds BasicIdeaofGAN MaximumLikelihoodEstimation Givenadatadistribution Wehaveadistribution parameterizedby E g isaGaussianMixtureModel aremeansandvariancesoftheGaussiansWewanttofind suchthat closeto Sample 1 2 from Wecancompute Likelihoodofgeneratingthesamples 1 Find maximizingthelikelihood MaximumLikelihoodEstimation max 1 max 1 max 1 max max min 1 2 from Howtohaveaverygeneral Now isaNN Itisdifficulttocomputethelikelihood BasicIdeaofGAN GeneratorGGisafunction inputz outputxGivenapriordistributionPprior z aprobabilitydistributionPG x isdefinedbyfunctionGDiscriminatorDDisafunction inputx outputscalarEvaluatethe difference betweenPG x andPdata x ThereisafunctionV G D min max Hardtolearnbymaximumlikelihood BasicIdea 1 2 3 1 2 3 1 GivenageneratorG max evaluatethe difference between and PicktheGdefining mostsimilarto min max max GivenG whatistheoptimalD maximizingGivenx theoptimalD maximizing 1 1 1 1 min max AssumethatD x canhaveanyvaluehere max Givenx theoptimalD maximizingFindD maximizing f a 1 1 f 1 11 1 0 1 11 1 min max a D b D 0 1 max 1 2 3 min max 1 1 2 2 1 1 difference between 1and max max 2 2 12 12 2 12 2 2 max 2log2 KLPdatax Pdatax PGx2 2 2 2 Jensen Shannondivergence 2 2 2 2 KLPGx Pdatax PGx2 max Intheend GeneratorG DiscriminatorDLookingforG suchthatGivenG max WhatistheoptimalG 2 2 2 0 log2 min max Algorithm TofindthebestGminimizingthelossfunction max 1 2 3 1 2 3 1 2 3 If isthemaxone min max definesG Algorithm Given 0Find 0 maximizing 0 0 Obtain 1Find 1 maximizing 1 1 Obtain 2 0 0 istheJSdivergencebetween and 0 1 1 istheJSdivergencebetween and 1 DecreaseJSdivergence DecreaseJSdivergence Algorithm Given 0Find 0 maximizing 0 0 Obtain 1 0 0 istheJSdivergencebetween and 0 DecreaseJSdivergence 0 0 1 0 0 0 1 0 smaller 1 1 Assume 0 1 Don tupdateGtoomuch Inpractice GivenG howtocomputemax Sample 1 2 from sample 1 2 fromgenerator 1 1 1 1 1 Maximize MinimizeCross entropy BinaryClassifier OutputisD x 1 2 from 1 2 from Disabinaryclassifier canbedeep withparameters Positiveexamples Negativeexamples Minimize 1 1 1 1 1 MinimizeCross entropy BinaryClassifier Outputisf x Ineachtrainingiteration Samplemexamples 1 2 fromdatadistribution Samplemnoisesamples 1 2 fromtheprior Obtaininggenerateddata 1 2 Updatediscriminatorparameters tomaximize 1 1 1 1 1 Sampleanothermnoisesamples 1 2 fromtheprior Updategeneratorparameters tominimize 1 1 1 1 1 Algorithm Repeatktimes LearningD LearningG Initialize forDand forG max Canonlyfindlowerfoundof OnlyOnce ObjectiveFunctionforGeneratorinRealImplementation Realimplementation labelxfromPGaspositive 1 1 Slowatthebeginning Demo Thecodeusedindemofrom IssueaboutEvaluatingtheDivergence EvaluatingJSdivergence MartinArjovsky L onBottou TowardsPrincipledMethodsforTrainingGenerativeAdversarialNetworks 2017 arXivpreprint EvaluatingJSdivergence JSdivergenceestimatedbydiscriminatortellinglittleinformation https arxiv org abs 1701 07875 WeakGenerator StrongGenerator Discriminator max 2 2 2 Reason1 Approximatebysampling 1 1 0 0 1 1 1 1 1 log2 Weakenyourdiscriminator CanweakdiscriminatorcomputeJSdivergence Discriminator max 2 2 2 Reason2 thenatureofdata 1 1 0 0 1 1 1 1 1 log2 Both and arelow dimmanifoldinhigh dimspace Usuallytheydonothaveanyoverlap Evaluation Better Evaluation 0 50 1 2 2 2 100 2 0 Notreallybetter AddNoise AddsomeartificialnoisetotheinputsofdiscriminatorMakethelabelsnoisyforthediscriminator and havesomeoverlap Discriminatorcannotperfectlyseparaterealandgenerateddata Noisesdecayovertime ModeCollapse ModeCollapse DataDistribution GeneratedDistribution ModeCollapse Whatwewant Inreality FlawinOptimization Maximumlikelihood minimizeKL ModifiedfromIanGoodfellow stutorial MinimizeKL reverseKL Reverse Thismaynotbethereason basedonIanGoodfellow stutorial SomanyGANs ConditionalGAN Motivation Generator ScottReed ZeynepAkata XinchenYan LajanugenLogeswaran BerntSchiele HonglakLee GenerativeAdversarialText to ImageSynthesis ICML2016 Text Image ScottReed ZeynepAkata SantoshMohan SamuelTenka BerntSchiele HonglakLee LearningWhatandWheretoDraw NIPS2016 HanZhang TaoXu HongshengLi ShaotingZhang XiaoleiHuang XiaogangWang DimitrisMetaxas StackGAN TexttoPhoto realisticImageSynthesiswithStackedGenerativeAdversarialNetworks arXivprepring 2016 Motivation Challenge NN Text Image apoint notadistribution