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Typical architectures of Generative AdversarialNetworks make use of a unimodal latent distribution transformed by a continuous generator.
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Improved techniques for training gans
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Wasserstein generative adversarial networks
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Disconnected manifold learning for generative adversarial networks
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Pacgan: The power of two samples in generative adversarial networks
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Spectral normalization for generative adversarial networks
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On the regularization of wasserstein GANs
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Improved training of wasserstein gans
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Deligan: Generative adversarial networks for diverse and limited data
Gurumurthy, S., Kiran Sarvadevabhatla, R., and Venkatesh Babu, R · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Stabilizing training of generative adversarial networks through regularization
Roth, K., Lucchi, A., Nowozin, S., and Hofmann, T · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C · 2017
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Adagan: Boosting generative models
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On the discriminative-generalization tradeoff in GANs
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Rethinking generative mode coverage: A pointwise guaranteed approach
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Discriminator rejection sampling
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Large scale GAN training for high fidelity natural image synthesis
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A style-based generator architecture for generative adversarial networks
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Improved precision and recall metric for assessing generative models
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Mmgan: Generative adversarial networks for multi-modal distributions
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Self-attention generative adversarial networks
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