cvpr18-Separating Style and Content for Generalized Style Transfer 外文学习材料

cvpr18-Separating Style and Content for Generalized Style Transfer 外文学习材料

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时间:2019-08-01

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1、SeparatingStyleandContentforGeneralizedStyleTransferYexunZhangYaZhangWenbinCaiShanghaiJiaoTongUniversityShanghaiJiaoTongUniversityMicrosoftzhyxun@sjtu.edu.cnyazhang@sjtu.edu.cnwenbca@microsoft.comAbstractStyleStyleReferenceSetRepresentation…Style��EncoderOutputNeuralstyletransferhas

2、drawnbroadattentioninre-��W��WDecodercentyears.However,mostexistingmethodsaimtoexplic-…Content��itlymodelthetransformationbetweendifferentstyles,andEncoderMixerContentReferenceSetContentthelearnedmodelisthusnotgeneralizabletonewstyles.RepresentationWehereattempttoseparatetherepresent

3、ationsforstylesFigure1.TheframeworkoftheproposedEMDmodel.andcontents,andproposeageneralizedstyletransfernet-cently,severalvariationsofgenerativeadversarialnetworksworkconsistingofstyleencoder,contentencoder,mixerand(GANs)[14,28]areintroducedbyaddingadiscriminatordecoder.Thestyleencod

4、erandcontentencoderareusedtothestyletransfernetworkwhichincorporatesadversarialtoextractthestyleandcontentfactorsfromthestyleref-losswithtransferlosstogeneratebetterimages.However,erenceimagesandcontentreferenceimages,respectively.thesestudiesaimtoexplicitlylearnthetransformationfrom

5、Themixeremploysabilinearmodeltointegratetheaboveacertainsourcestyletoagiventargetstyle,andthelearnedtwofactorsandfinallyfeedsitintoadecodertogeneratemodelisthusnotgeneralizabletonewstyles,i.e.retrainingimageswithtargetstyleandcontent.Toseparatethestyleisneededfortransformationsofnewst

6、yleswhichistime-featuresandcontentfeatures,weleveragetheconditionalconsuming.dependenceofstylesandcontentsgivenanimage.DuringInthispaper,weproposeanovelgeneralizedstyletrans-training,theencodernetworklearnstoextractstylesandfernetworkwhichcanextendwelltonewstylesorcontents.contentsfr

7、omtwosetsofreferenceimagesinlimitedsize,Differentfromexistingsupervisedstyletransfermethods,onewithsharedstyleandtheotherwithsharedcontent.whereanindividualtransfernetworkisbuiltforeachpairThislearningframeworkallowssimultaneousstyletrans-ofstyletransfer,theproposednetworkrepresentse

8、achstylefera

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