Image Generation and Editing with Variational Info Generative AdversarialNetworks

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by  Mahesh Gorijala

Image Generation and Editing with Variational Info Generative Adversarial
Networks
MaheshGorijala [email protected]
AmbedkarDukkipati [email protected]
DepartmentofComputerScienceandAutomation
IndianInstituteofScience,Bangalore-560012,India
7
1
0 Abstract pass above mentioned challenges by using deep neural
2
networksasparametrizedfunctionsthatgeneratesamples.
Recently there has been an enormous interest
n Few dominant approaches that emerged in recent years
in generative models for images in deep learn-
a areVariationalAutoEncoders(VAEs)(Kingma&Welling,
J ing. In pursuit of this, Generative Adversarial
7 Networks (GAN) and Variational Auto-Encoder 2013), Generative Adversarial Networks (GANs) (Good-
fellowetal.,2014),GenerativeStochasticNetworks(Ben-
1 (VAE) have surfaced as two most prominent
gioetal.,2014),DeepRecurrentAttentionWriter(Gregor
and popular models. While VAEs tend to pro-

] et al., 2015), Pixel Recurrent Neural Networks (van den
V duce excellent reconstructions but blurry sam-
Oord et al., 2016b) and Pixel Convolutional Neural Net-
ples, GANsgeneratesharpbutslightlydistorted
C works (van den Oord et al., 2016a). Generative mod-
images. In this paper we propose a new model
. els have also helped to set benchmark results in semi-
s called Variational InfoGAN (ViGAN). Our aim
c supervised learning (Kingma et al., 2014; Radford et al.,
is two fold: (i) To generated new images con-
[ 2015). Lately there have been attempts to use the ad-
ditioned on visual descriptions, and (ii) mod-
1 ify the image, by fixing the latent representa- vances in deep generative models for semi-supervised
v learning(Kingmaetal.,2014;Chenetal.,2016)andjoint
tionofimageandvaryingthevisualdescription.
8 modeling of images and their visual descriptions (Reed
We evaluate our model on Labeled Faces in the
6 etal.,2016;Yanetal.,2015).
5 Wild (LFW), celebA and a modified version of
4 MNIST datasets and demonstrate the ability of In this work, we address the problem of jointly modeling
0 our model to generate new images as well as to images and their visual descriptions through deep genera-
.
1 modifyagivenimagebychangingattributes. tive models. Multimodal learning requires learning a cor-
0 respondence between data across different modalities. In
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deep learning context, the first works towards multimodal
1
1.Introduction learningisbyN.Srivatsavaetal.(Srivastava&Salakhutdi-
:
v nov,2012)andJ.Nigametal.(Ngiametal.,2011). These
i Generative models, that are essential part of unsupervised
X models pose this problem as learning cross-modal repre-
learning, capture the structure/patterns in data by learning
sentationsatlatentfeaturelevelandsomemodelseventry
r
a togeneratesamplesthatresemblethetrainingdata. Earlier tolearnasharedrepresentationacrossmodalities. Thisof-
workingenerativemodelingfocusedongraphicalmodels
ten becomes complex, because one modality rarely con-
and energy based models with latent variables, for exam-
tains all the information about the other modality. In the
ple,RestrictedBoltzmannMachines(RBM)(Bengioetal.,
case of images and text, the information present in image
2011; Hinton, 2010), Deep Belief Networks (DBN) (Hin-
modality is much more dense than the information in text
ton et al., 2006). In these models, exact inference, nor-
modality.
malization constant and its gradients are intractable and
samplesareusuallyobtainedwithexpensiveMarkovChain VAEs are trained by maximizing the lower bound to the
MonteCarlo(MCMC)techniques. log-likelihood of the data and they include an inference
networkaspartofthetrainingprocedure. Extensionssuch
Recent advances in deep learning have enabled us to by-
as (Burda et al., 2015) are proposed to increase the tight-
ness of the lower bound. On the other hand, GANs are
trainedasamini-maxgamebetweenthegeneratoranddis-
criminator. TrainingofGANsonlyrequireadifferentiable
ImageGenerationandEditingwithVariationalInfoGenerativeAdversarialNetworks
mapping from a latent space to the data space, without where q(z|x) is an approximation to the true posterior
anyrequirementforinferencenetwork. Recentlyproposed p(z|x). ThelossfunctionforVAEisgivenas
InfoGAN (Chen et al., 2016) framework, a variation of
L =L +L , (2)
GAN (Goodfellow et al., 2014), defines image generation VAE recon prior
asafunctionoftwosetsofvariablesandisshowntolearn whereL representreconstructionlossandL rep-
recon prior
interpretablerepresentationsofimages.Thisisachievedby resents KL divergence between the approximate posterior
including a mutual information objective between a small q(z|x)andthepriorp(z).
subset of interpretable latent variables (denoted by c) and
theobservations.ButasinGANs,italsolacksaninference 2.2.GenerativeAdversarialNetwork(GAN)
mechanismtoinferthelatentstategivenanimage.
GAN (Goodfellow et al., 2014) consist of two networks,
In this paper, we propose a new model, Variational Info namely, generator (G) and discriminator (D). Generator
GAN (ViGAN) that combines the InfoGAN model with networktakesnoisez drawnfromapriordistributionp(z)
VAE by using a subset of latent variables to represent the
as input and give an image as output. Discriminator net-
description of images. We also extend the architecture to
workgivestheprobabilitythattheinputimageisreal.
includeaninferencemechanismandpresentatrainingpro-
ceduretojointlytraintheinferencenetworkandthemodel. TheGANobjectiveistotraindiscriminatortodistinguish
Weprovideatrainingandsamplingprocedureforthepro- betweenrealandfakedatasamples, whilesimultaneously
posedmodel. WedenonstrateViGAN’sabilitytogenerate trainingthegeneratortofooldiscriminator. Lossesforthe
new images conditioned on attributes, as well as modify- modelaregivenas
ing given images by changing attributes on three datasets
L =E [−logD(G(z))], and (3)
gen z∼p(z)
namely,MNIST(LeCunetal.,1998),LFW(Huangetal.,
L =E [logD(G(z))]+E [−logD(x)],
2007),celebA(Liuetal.,2015). dis z∼p(z) x∼p(x)
(4)
2.BackgroundMaterial whereLgenandLdisrepresentgeneratoranddiscriminator
lossesrespectively. Duringtraining,parametersofgenera-
VAEsandGANsarebasedontheideaofusingadifferen- toranddiscriminatorareupdatedalternatively.
tiable neural network to transform latent variables to data
InfoGAN(Chenetal.,2016)extendsGANbymaximizing
samples. While the training procedure for VAE includes
mutualinformationbetweenasmallsetoflatentvariables
an approximate inference network, GAN is trained via an
andobservations. ThiscanbeincorporatedintoGANob-
auxiliarydiscriminatornetwork,thatlearnstodiscriminate
jectivebyaddingalosstermrepresentingthemutualinfor-
realdatasamplesfromgeneratedsamples. Inthissection,
mation. Thisensuresthattheinformationrepresentedbya
we briefly describe previous work on VAE, GAN and In-
setof“interpretablelatentvariables”(c),isnotlostduring
foGANonwhichourmodelbuildsupon. InSection3,we
generationprocess. Let[z,c]betheinputtothegenerator.
developourproposedmodelusingthenotationsintroduced
ThenmutualinformationbetweencandG(z,c)is
inthissection.
I(c;G(z,c)=H(c)−H(c|G(z,c)) (5)
2.1.VariationalAuto-encoder(VAE) ≥H(c)+E [E [− log Q(c|x)]],
x∼G(z,c) c∼P(c|x)
(6)
AVAE(Kingma&Welling,2013)worksontheprinciple
of maximizing lower bound to the marginal likelihood of where Q(c|x) is approximation to P(c|x). This lower
the data. In simple terms, VAE can be viewed as an au- bound can be maximized directly. Hence recognition loss
toencoder with a prior over latent variables. It consists of canbedefinedas
two networks, encoder, a mapping from data space X to
latentspaceZ anddecoder,areversemappingfromlatent Lrecog =−Ex∼G(z,c)[Ec∼P(c|x)[− log Q(c|x)]]. (7)
spaceZ todataspaceX .
NowthemodifiedGANobjectivecanbewrittenas
Let p(x|z) represent the probability distribution
parametrized by the decoder, p(z) represent the prior Lgen =Ez∼p(z)[− log D(G(z))]+Lrecog, and (8)
over the latent space and p(x) represent probability of L =E [log D(G(z))]+E [− log D(x)].
dis z∼p(z) x∼p(x)
the data point x. Then upper bound on the negative (9)
log-likelihoodofadatapointxcanbewrittenas
The recognizer Q(c|x) is realized by a neural network. It
can be made to share parameters with the discriminator.
−logp(x)≤−E logp(x|z)+KL(q(z|x)||p(z))
q(z|x) Thesetofinterpretablelatentvariablesccaneitherbecon-
≤L +L , (1)
recon prior tinuous,categoricaloracombinationofboth.
ImageGenerationandEditingwithVariationalInfoGenerativeAdversarialNetworks
3.ProposedMethod Finally, the discriminator is trained with three sets of ex-
ampleswithimagesfromtheactualdatasettreatedasreal
3.1.Motivation
examples and generated and reconstructed images treated
MutualinformationobjectiveofInfoGANensuresthatthe asfakeexamples. Discriminatorloss,Ldisis
information represented by the interpretable latent vari-
L =E [log D(G(z,c))]
ables(c),ispreservedduringthegenerationprocess. Butit dis z∼q(z|x),c∼p(c|x)
hasnocontrolovertheinformationthatcrepresents.Inthis +Ez∼p(z),c∼p(c)[log D(G(z,c))]
paper, we propose a method to jointly model images and E [− log D(x)]. (15)
x∼p(x)
theirdescriptionsbyusingctorepresenttheknownaspects
ofthedata, andz torepresentalltheremainingfactorsof Giventheselosses,stepsfortrainingtheViGANisasfol-
variation that are not captured by the attributes c. This lows:
can done by providing (z˜,c) as an input to the generator,
wherez˜istherepresentationofimageobtainedbypassing 1. minimizeL w.r.ttoparametersofencoder,
enc
itthroughanencoder. Inthissetup,weassumethatthede-
2. minimizeL w.r.ttoparametersofgenerator,
scription c is available for all the images. This approach gen
differs from that of InfoGAN, where no labelled data is 3. minimizeLrecogw.r.ttoparametersofrecognizer,and
usedandothersemi-supervisedapproaches(Kingmaetal., 4. minimizeL w.r.ttoparametersofdiscriminator.
dis
2014), inwhichonlyasubsetofdataisassumedtobela-
belled.
This setup with VAE and GAN sharing common de-
Theencoder-generatorcombinationwithaprioroverz˜can coder/generatorissimilartothemodeldescribedin(Larsen
bethoughtofasanVAE,whereasgenerator-discriminator et al., 2015). However, the decoder/generator conditioned
combination can be taken to be GAN. Schematic repre- on encoded representation of the image and the attribute
sentation of the network architecture is presented in Fig- vector. Further, to improve the perceptual quality of im-
ure1. Expectationsovertheapproximateposteriorq(z|x) ages, we consider the reconstruction loss in the hidden
are computed using the reparametrization trick presented space of the discriminator instead of the pixel space as in
in(Kingma&Welling,2013). (Larsenetal.,2015).
OneofthemainadvantageofViGANisthatthesamenet-
3.2.TrainingandSampling work can be used to generate new images conditioned on
attributesaswellmodifyagivenimagebyfixingtheimage
ThelossfortheencodernetworkL issumofreconstruc-
enc
representation and changing attributes. The procedure for
tionlossL andtheKLdivergenceL asdescribed
recon prior
thisisdescribedbelow.
intheprevioussection:
L =L +L , (10)
enc prior recon • To generate new images, sample z from p(z)and c
Lprior =KL(q(z|x)||p(z)), and (11) fromP(c|x). Thenpasstheconcatenatedvector[z,c]
L =−E logP(x|z,c). (12) throughgenerator.
recon z∼q(z|x),c∼P(c|x)
Loss for the recognition network Q, denoted as Lrecog is • To modify a image, first get representation of image
computedusingonlythesamplesfromtheactualdataset. z bybypassingitthroughencoder. Thensamplethe
L =−E [E [− log Q(c|x)]] (13) desiredattributesc.Thenpasstheconcatenatedvector
recog x∼P(x) c∼P(c|x)
[z,c]throughgenerator.
Lossforthegenerator/decoderiscomprisedofthreeparts.
GAN loss for reconstructed samples and generated sam-
4.Experiments
ples,recognitionlossforreconstructedsamplesandgener-
atedsamples,andthereconstructionloss.
4.1.Datasets
L =E [−logD(G(z,c))
gen z∼q(z|x),c∼p(c|x) Wetrainedtheproposedmodelonfollowingthreedatasets.
−λ logQ(c|G(z,c)]
2
+E [− log D(G(z,c))
z∼p(z),c∼p(c) 1. MNIST:WecreatedamodifiedversionofMNISTby
−λ logQ(c|G(z,c)] placingtwodigitson64x64grid.Firstdigitisplaced
2
+λ L , (14) randomly in left half of the grid and second digit in
1 recon
righthalfofthegrid.
whereλ andλ arethehyperparameters,thatquantifythe
1 2
relativeimportancebetweenreconstructionandrecognition 2. CelebA:Thisdatasetconsistsofaround200,000im-
objectives. ages of human faces. Each images is paired with 40
ImageGenerationandEditingwithVariationalInfoGenerativeAdversarialNetworks
Figure1.Detailedschematicdiagramoftheproposednetwork.TheproposednetworkcombinesthearchitecturesofVAEandInfoGAN
binary attributes such as male, smiling, eyeglasses,
mustacheetc. Weresizedeachimageto64x64pix- Generator/Decoder
els. Input: 256+40forcelebA,256+73forLFW,
256+20forMNIST
4x4x448fullyconnected,relu,batchnorm
3. LFW: This dataset consists of around 13,000 images
4x4x256deconv,stride2,relu,batchnorm
ofhumanfacespaired73realvaluedvisualattributed.
4x4x128deconv,stride2,relu
Wecroppedandscaledeachimageto64x64pixels.
4x4x64deconv,stride2,relu
4x4x3(1)deconv,stride2,tanh
4.2.TrainingDetails
Discriminator Recognizer
We used a similar architecture for all three datasets, with Input: 64x64x3or64x64x1image
appropriatedchangestothedimensionoftheattributevec- 4x4x64conv,stride2,leakyrelu
tor and number of image channels (one channel for mod- 4x4x128conv,stride2,leakyrelu,batchnorm
ified MNIST and three for other two datasets). Detailed 4x4x256conv,stride2,leakyrelu,batchnorm
architectures for encoder, generator and discriminator are
Discriminator: 1fully-connected
presentedinthefollowingtables
Recognizer: 128fullyconnected,leakyrelu,batchnorm
40fullyconnected,sigmoidforcelebA
73fullyconnected,sigmoidforLFW
Encoder
20fullyconnected,sigmoidforMNIST
Input: 64x64x3or64x64x1image
4x4x64conv,stride2,leakyrelu
For optimization, Adam optimizer (Kingma & Ba, 2014)
4x4x128conv,stride2,leakyrelu,batchnorm
is used for all the networks, with learning rates of 0.001,
4x4x256conv,stride2,leakyrelu,batchnorm
0.001,0.0002,0.0002forencoder,generator,discriminator
512fullyconnected,leakyrelu,batchnorm
andrecognizerrespectively.
512fullyconnected,tanh,batchnorm
We observed the ability of the network to modify images,
to be very sensitive to the choice of hyper parameters λ
1
ImageGenerationandEditingwithVariationalInfoGenerativeAdversarialNetworks
andλ . Suitablechoiceofλ andλ isveryimportantto
2 1 2
strike a balance between ability of network to accurately
reconstruct the input and at the same time be sensitive to
changesintheattributes.
(a)Firstdigitreplacedby0
4.3.Results
Below we demontrate the ability of network to gener-
ate new images, as well as to modify a given image by
changingtheattributes. Figure2,showsthesamplesfrom
MNISTdatasetandtheircorrespondingreconstructions.
(b)SamplesfrommodifiedMNISTdataset
(a)SamplesfrommodifiedMNIST
(c)Firstdigitreplacedby1
Figure3.Modification results on modified MNIST dataset with
firstdigitreplacedby1and0intopandbottomrowsrespectively,
whilepreservingthelocationofboththedigits.Middlerowshows
(b)ReconstructionsonmodifiedMNIST
theactualimages.
Figure2. ReconstructionresultsonMNISTdataset
Further some extensions of above mentioned models are
In Figure 3 and Figure 4, we demonstrate the capability proposedtogenerateimagesfromtext. Someexamplesof
of the proposed model to modify images according to the suchmodelsare(Mansimovetal.,2015;Reedetal.,2016;
changes in attributes, while keeping other features intact. Mirza&Osindero,2014). Thesemodelscangenerateim-
InFigure3. themiddlerowshowstheactualimages,while agesgivenattributes,butcannotmodifyagivenimage.
in top and bottom rows, first digit is replaced by 0 and 1
(Larsen et al., 2015) proposed a method to modify faces,
respectively. Similarly in Figure 4, the middle row shows
byaddingorsubtractingameanvisualattributevector.But
the actual images, while in top and bottom rows, second
themethodisapplicableforonlybinaryattributes.(Pandey
digitisreplacedby2and7respectively.
&Dukkipati,2016)proposesanothermethodbasedonem-
In Figure 6, eyeglasses are added to the base image from beddingvisualrepresentationsandattributedescriptionsin
celebA dataset by changing the corresponding attribute acommonspace.
value to 1. Figure 7. shows the modification results on
RecentlyReedet.al(Reedetal.,2016)alsoproposedasim-
LFW dataset with real valued attributes. In Figure 4. we
ilarmethodbasedforconditionalimagegeneration. They
present four variations of two base images, generated by
too demonstrate image modification with their network.
thenetwork,correspondingtotheattributesfrowning,smil-
Buttheylearntheposteriorseparatelyfromthegeneration
ingwithclosedmouth,smilingwithopenmouthandwide
process, while our method learns posterior along with the
eyes.
generationnetwork
5.ConcludingRemarks Inthework,weproposedanewarchitectureforjointmod-
elingofimagesandtheirvisualdescriptionsbycombining
Severaldeepgenerativemodelshavebeenproposedinthe theideasfromVariationalAutoEncoders(VAEs)andInfo-
recentyears. SomeofthesuccessfulapproachesareVaria- GAN.Wedemonstratetheabilityoftheproposedmodelto
tionalAutoEncoders(VAEs),GenerativeAdversarialNet- generatenewimagesaswellastomodifyexistingimages.
works(GANs),DeepRecurrentAttentionWriter(DRAW)
, Pixel Recurrent Neural Networks (PixelRNN). All these
frameworksareusuallytrainedwithbackpropagation.
ImageGenerationandEditingwithVariationalInfoGenerativeAdversarialNetworks
(a)Seconddigitreplacedby2 (a)SamplesfromVAE
(b)SamplesfrommodifiedMNISTdataset
(b)SamplesfromGAN
(c)Seconddigitreplacedby7 (c)Samplesfromtheproposedmodel
Figure5.Qualitative comparison between VAE, GAN and the
Figure4.Modification results on modified MNIST dataset with
proposedmodel. Samplesfromproposedmodelappearmorere-
seconddigitreplacedby2and7intopandbottomrowsrespec-
alisticthanthesamplesfromVAEandGAN
tively, while preserving the location of both the digits. Middle
rowshowstheactualimages.
InAdvancesinNeuralInformationProcessingSystems,
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