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Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.
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Photo-realistic single image super-resolution using a generative adversarial network
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Dualing GANs
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Optimal robust smoothing extragradient algorithms for stochastic variational inequality problems
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f-GAN: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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Stochastic variance reduction methods for saddle-point problems
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Training GANs with optimism
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
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Which training methods for GANs do actually converge?
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Spectral normalization for generative adversarial networks
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Negative momentum for improved game dynamics
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The unusual effectiveness of averaging in GAN training
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