Generative Adversarial Networks - INF5860

88 Pages·2017·22.31 MB·English

generative adversarial networks
INF5860—MachineLearningforImageAnalysis
Ole-JohanSkrede
02.05.2018
UniversityofOslo
Outline
∙ Repetition
∙ GenerativeAdversarialNetworks
∙ Otheradversarialmethods
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repetition
Autoencoders
∙ Anautoencoderf consistofanencoderg andandecoderh
∙ Theencodermapstheinputxtosomerepresentationz
g(x)=z
∙ Weoftencallthisrepresentationz forthecodeorthelatentvector
∙ Thedecodermapsthisrepresentationz tosomeoutputx^
g(z)=x^
∙ Wewanttotraintheencoderanddecodersuchthat
f(x)=h(g(x))=x^(cid:25)x
∙ Commonlyusedforcompression,featureextractionand
de-noising
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Compression autoencoder — MNIST example
(a)Original (b)Reconstructed
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De-noising autoencoder — MNIST example
(a)Original (b)Reconstructed
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Variational autoencoders
∙ Avariationalautoencoderisdesignedtohavea
continuouslatentspace
∙ Thismakesthemidealforrandomsamplingand
interpolation
∙ Itachievethisbyforcingtheencoderg togenerate
Gaussianrepresentations,z (cid:24)N((cid:22);(cid:27)2)
∙ Moreprecisely,foroneinput,theencodergeneratesa
mean(cid:22)andavariance(cid:27)2
∙ Wesamplethensampleazero-mean,unit-variance
Gaussianz~(cid:24)N(0;1)
∙ Constructtheinputz tothedecoderfromthis
z =(cid:22)+z~(cid:27)2
∙ Withthis,z issampledfromq =N((cid:22);(cid:27)2)
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Intuition
∙ Thisisastochasticsampling
∙ Thatis,wecansampledifferentz fromthesamesetof
((cid:22);(cid:27)2)
∙ Theintuitionisthatthedecoder“learns”thatforagiven
inputx:
∙ thepointzisimportantforreconstruction
∙ butalsoaneighbourhoodofz
∙ Inthisway,wehavesmoothedthelatentspace,atleast
locally
∙ Inthepreviouslecture,welearntwaystoachievethis
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VAE example: reconstruction
(a)Original (b)Reconstructed
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VAE example: generation of new signals
∙ Samplearandomlatentvectorz fromN(0;1)
∙ Decodez
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