Generative Adversarial Learning: Architectures and Applications

362 Pages·2022·13.435 MB·English

by  Roozbeh Razavi-Far, Ariel Ruiz-Garcia, Vasile Palade, Juergen Schmidhuber

Intelligent Systems Reference Library 217
Roozbeh Razavi-Far
Ariel Ruiz-Garcia
Vasile Palade
Juergen Schmidhuber   Editors
Generative
Adversarial
Learning:
Architectures
and Applications
Intelligent Systems Reference Library
Volume 217
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· ·
Roozbeh Razavi-Far Ariel Ruiz-Garcia
·
Vasile Palade Juergen Schmidhuber
Editors
Generative Adversarial
Learning: Architectures
and Applications
Editors
RoozbehRazavi-Far ArielRuiz-Garcia
DepartmentofElectricalandComputer SeeChange.ai
EngineeringandSchoolofComputer Manchester,UK
Science
UniversityofWindsor JuergenSchmidhuber
Windsor,ON,Canada TheSwissAILab,IDSIA
UniversityofLugano,USI&SUPSI
VasilePalade Lugano,Switzerland
CentreforComputationalScience
andMathematicalModelling
CoventryUniversity
Coventry,UK
ISSN1868-4394 ISSN1868-4408 (electronic)
IntelligentSystemsReferenceLibrary
ISBN978-3-030-91389-2 ISBN978-3-030-91390-8 (eBook)
https://doi.org/10.1007/978-3-030-91390-8
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Preface
The recent big advancements in Generative Adversarial Networks (GANs) have
openedcountlessopportunitiesforsyntheticdatagenerationinmanyareas,including
science, engineering, medicine, security, among others. Generative Adversarial
Networks are built based on two dueling neural networks, in which one network
aimstogeneratenewsamplesandconvincethecompetingnetworkthatthegenerated
samplesarerealandnotsynthetic.
This book contains recent theoretical advancements on Generative Adversarial
Networks and their interdisciplinary applications, such as for fair data generation,
imagegeneration,faceaging,intrusiondetection,industrialdefectdetection,action
recognition,drugdiscovery,medicalimaging,amongotherapplications.
Thebookisintendedforscientists,researchers,educators,andanyoneinterested
in the latest methodological advancements on Generative Adversarial Networks
and their applications. In addition, the book can be considered as a reference for
variousstate-of-the-artarchitecturesofGANs,majortrainingchallengesofGANs
including convergence, vanishing gradients, and mode collapse, as well as the
technicalremediestoovercomethesechallenges.
The editors would like to convey many thanks to those who contributed to this
book by sharing their insights and achievements in this emerging area in machine
learning.
WearealsoverygratefulfortheassistanceprovidedbySpringer-Verlag.
Ontario,Canada RoozbehRazavi-Far
Manchester,UK ArielRuiz-Garcia
Coventry,UK VasilePalade
Lugano,Switzerland JuergenSchmidhuber
v
Contents
1 An Introduction to Generative Adversarial Learning:
ArchitecturesandApplications ................................. 1
RoozbehRazavi-Far,ArielRuiz-Garcia,andVasilePalade
1.1 BookOutline ............................................ 2
References .................................................... 4
2 GenerativeAdversarialNetworks:ASurveyonTraining,
Variants,andApplications ..................................... 7
MaryamFarajzadeh-Zanjani,RoozbehRazavi-Far,MehrdadSaif,
andVasilePalade
2.1 Introduction ............................................. 7
2.2 GANVariants ........................................... 11
2.2.1 OriginalGenerativeAdversarialNetwork(GAN) ...... 11
2.2.2 Deep Convolutional Generative Adversarial
Network(DCGAN) ............................... 13
2.2.3 Conditional Generative Adversarial Network
(CGAN) ......................................... 13
2.2.4 InformationMaximizingGenerativeAdversarial
Networks(InfoGAN) .............................. 15
2.2.5 Auxiliary Classifier Generative Adversarial
Network(ACGAN) ............................... 16
2.2.6 Stacked Generative Adversarial Networks
(StackGAN) ..................................... 16
2.2.7 Cycle-ConsistentGenerativeAdversarialNetwork
(CycleGAN) ..................................... 16
2.2.8 Wasserstein Generative Adversarial Network
(WGAN) ........................................ 18
2.2.9 Semi-SupervisedGenerativeAdversarialNetwork
(SSGAN) ........................................ 18
2.2.10 Progressive Growing Generative Adversarial
Network(ProgressiveGAN) ........................ 19
vii
viii Contents
2.2.11 Style-Based Generative Adversarial Network
(StyleGAN) ...................................... 19
2.2.12 BidirectionalGenerativeAdversarialNetwork
(BiGAN) ........................................ 20
2.2.13 Bayesian Generative Adversarial Network
(BGAN) ......................................... 20
2.3 GANApplications ........................................ 20
2.4 Conclusions ............................................. 23
References .................................................... 23
3 Fair Data Generation and Machine Learning Through
GenerativeAdversarialNetworks ............................... 31
XintaoWu,DepengXu,ShuhanYuan,andLuZhang
3.1 Introduction ............................................. 31
3.2 OverviewofFairGANFramework .......................... 33
3.2.1 DefinitionsandMetricsofFairness .................. 33
3.2.2 FairGANFramework .............................. 35
3.3 Simplified Fairness Aware Generative Adversarial
Networks ............................................... 35
3.3.1 ModelFrameworkDesign .......................... 35
3.3.2 EmpiricalEvaluation .............................. 37
3.4 AchievingCausalFairnessinDataGeneration ................ 38
3.4.1 ModelFrameworkDesign .......................... 38
3.4.2 AchievingDirect/Indirect/CounterfactualFairness ..... 40
3.4.3 EmpiricalEvaluation .............................. 42
3.5 AchievingFairnessinClassification ......................... 42
3.5.1 ModelFrameworkDesign .......................... 43
3.5.2 AchievingFairClassification ....................... 44
3.5.3 EmpiricalEvaluation .............................. 45
3.6 RelatedWork ............................................ 46
3.6.1 DealingwithDifferentTypesofStructuralData ....... 46
3.6.2 DealingwithPrivacy .............................. 47
3.7 FutureDirections ......................................... 48
3.7.1 VariantsComparisonandArchitectureDesign ........ 48
3.7.2 Achieving Long-Term Fairness in Dynamic
DecisionMaking ................................. 49
3.7.3 AchievingFairnessinRegression ................... 49
3.7.4 AchievingFairnessinRecommendation .............. 50
3.7.5 OpenSourceSoftware ............................. 51
3.8 Conclusions ............................................. 51
References .................................................... 51
Contents ix
4 QuaternionGenerativeAdversarialNetworks ................... 57
EleonoraGrassucci,EdoardoCicero,andDaniloComminiello
4.1 Introduction ............................................. 58
4.2 QuaternionAlgebra ....................................... 59
4.3 GenerativeLearningintheQuaternionDomain ............... 61
4.3.1 TheQuaternionAdversarialFramework .............. 62
4.3.2 QuaternionFullyConnectedLayers ................. 63
4.3.3 QuaternionConvolutionalLayers ................... 64
4.3.4 QuaternionPoolingLayers ......................... 65
4.3.5 QuaternionBatchNormalization .................... 67
4.3.6 QuaternionSpectralNormalization .................. 69
4.3.7 QuaternionWeightInitialization .................... 71
4.3.8 Training ......................................... 72
4.4 GANArchitecturesintheQuaternionDomain ................ 72
4.4.1 VanillaQGAN ................................... 73
4.4.2 AdvancedQGAN ................................. 73
4.4.3 EvaluationMetrics ................................ 75
4.5 ExperimentalEvaluation .................................. 76
4.5.1 EvaluationofSpectralNormalizationMethods ........ 82
4.6 Conclusions ............................................. 84
References .................................................... 84
5 ImageGenerationUsingContinuousConditionalGenerative
AdversarialNetworks ......................................... 87
XinDing, YongweiWang, ZuhengXu, WilliamJ.Welch,
andZ.JaneWang
5.1 IntroductionandMotivation ............................... 88
5.2 ContinuousConditionalGenerativeAdversarialNetworks ...... 89
5.2.1 DerivationofHVDLandSVDLLosses .............. 89
5.2.2 ARuleofThumbforHyper-parameterSelection ...... 94
5.2.3 AlgorithmsforTrainingCcGANs ................... 95
5.3 TheoreticalAnalysis ...................................... 95
5.4 Experiments ............................................. 98
5.4.1 CaseStudy1:Circular2-DGaussians ............... 98
5.4.2 CaseStudy2:UTKFace ........................... 103
5.5 Conclusion .............................................. 112
References .................................................... 112
6 GenerativeAdversarialNetworksforDataAugmentation
inHyperspectralImageClassification ........................... 115
DimitraKoumoutsou, GeorgiosSiolas, EleniCharou,
andGeorgiosStamou
6.1 Introduction ............................................. 116
6.1.1 Dataset .......................................... 117
6.1.2 DataAvailability .................................. 119
6.1.3 ClassImbalance .................................. 119
x Contents
6.1.4 DataAugmentation ............................... 120
6.2 PreviousWork ........................................... 121
6.2.1 DataAugmentation ............................... 121
6.2.2 ClassImbalance .................................. 122
6.2.3 ApplicationsinHyperspectralImaging ............... 122
6.3 WassersteinGAN ........................................ 124
6.4 ConditionalWassersteinGenerativeAdversarialNetwork
withGradientPenaltyforHyperspectralImageGeneration ..... 127
6.4.1 Wasserstein Generative Adversarial Network
withGradientPenalty ............................. 127
6.4.2 HyperspectralDataPatch .......................... 128
6.4.3 DimensionalityReduction ......................... 129
6.4.4 DiscriminatorandGeneratorModels ................ 130
6.4.5 TrainingProcess .................................. 131
6.5 ExperimentalResults ..................................... 132
6.5.1 EvaluationMetrics ................................ 132
6.5.2 ExperimentalSetting .............................. 133
6.5.3 SpectralSignature ................................ 135
6.5.4 DataAugmentation ............................... 137
6.5.5 Visualizations .................................... 139
6.6 ConclusionandFutureWork ............................... 141
References .................................................... 142
7 FaceAgingUsingGenerativeAdversarialNetworks .............. 145
BrunoKemmer,RodolfoSimões,andClodoaldoLima
7.1 Introduction ............................................. 146
7.2 GenerativeAdversarialNetworks ........................... 147
7.2.1 ModeCollapse ................................... 148
7.2.2 GANsTypes ..................................... 149
7.2.3 ReferenceArchitecturesforFacialImaging ........... 151
7.3 DatabasesUsedforFacialAging ........................... 157
7.3.1 FG-NET ......................................... 157
7.3.2 UTKFace ........................................ 157
7.3.3 CACD .......................................... 157
7.3.4 MORPH ......................................... 158
7.3.5 IMDB-Wiki ...................................... 158
7.3.6 Cross-AgeLFW(CALFW) ........................ 158
7.3.7 OtherDatabases .................................. 159
7.4 ExperimentsandResults .................................. 159
7.4.1 CAAEExperiments ............................... 159
7.4.2 IPCGANExperiments ............................. 161
7.4.3 RecursiveChainingofReversibleImage-to-Image
TranslatorsExperiments,RCRIIT ................... 161
7.4.4 MethodComparison .............................. 163
7.5 Summary ............................................... 166
References .................................................... 166