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Wasserstein Generative Adversarial Networks

著者: Martín Arjovsky, Soumith Chintala, Léon Bottou - International Conference on Machine Learning, ICML 2017 被引用: 9,668

Concrete Representationof Abstract (M)-Spaces…Concrete Representation of Abstract (M)-Spaces (A characterization of the Space of Continuous Functions)Annealed importancesamplingAnnealed importance samplingEnvelope Theorems forArbitrary Choice SetsEnvelope Theorems for Arbitrary Choice SetsLSUN: Construction of aLarge-scale Image…LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the LoopTraining generativeneural networks via…Training generative neural networks via Maximum Mean Discrepancy optimizationHow (not) to Train yourGenerative Model…How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?UnsupervisedRepresentation Learning…Unsupervised Representation Learning with Deep Convolutional Generative Adversarial NetworksEnergy-based GenerativeAdversarial NetworkEnergy-based Generative Adversarial NetworkWasserstein Training ofRestricted Boltzmann…Wasserstein Training of Restricted Boltzmann MachinesUnrolled GenerativeAdversarial NetworksUnrolled Generative Adversarial NetworksGAN（GenerativeAdversarial Nets）GAN（Generative Adversarial Nets）Inverting The GeneratorOf A Generative…Inverting The Generator Of A Generative Adversarial NetworkCoulomb GANs: ProvablyOptimal Nash Equilibria…Coulomb GANs: Provably Optimal Nash Equilibria via Potential FieldsDA-GAN: Instance-LevelImage Translation by…DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial NetworksPrescribed GenerativeAdversarial NetworksPrescribed Generative Adversarial NetworksLearning GenerativeModels across…Learning Generative Models across Incomparable SpacesImage2StyleGAN: How toEmbed Images Into the…Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?Leveraging the InvariantSide of Generative…Leveraging the Invariant Side of Generative Zero-Shot LearningGenerative AdversarialNetworks (GANs)…Generative Adversarial Networks (GANs): Challenges, Solutions, and Future DirectionsLoss Functions ofGenerative Adversarial…Loss Functions of Generative Adversarial Networks (GANs): Opportunities and ChallengesmicrobatchGAN:Stimulating Diversity…microbatchGAN: Stimulating Diversity with Multi-Adversarial DiscriminationSmoothness and Stabilityin GANsSmoothness and Stability in GANsReview onself-supervised image…Review on self-supervised image recognition using deep neural networksWasserstein GenerativeAdversarial NetworksWasserstein Generative Adversarial Networks過去の参考文献中心の論文この論文を引用する論文古い新しい

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