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We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.
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Photo-realistic single image super-resolution using a generative adversarial network
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Least squares generative adversarial networks
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Conditional image synthesis with auxiliary classifier gans
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Automatic differentiation in pytorch
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Generating natural adversarial examples
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Reducing noise in gan training with variance reduced extragradient
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Your local gan: Designing two dimensional local attention mechanisms for generative models
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Prescribed generative adversarial networks
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Discriminator rejection sampling
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Large scale gan training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2018
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Sgan: An alternative training of generative adversarial networks
T. Chavdarova and F. Fleuret · 2018
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C. Donahue, J. McAuley, and M. Puckette · 2018
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Synthesizing audio with generative adversarial networks
C. Donahue, J. McAuley, and M. Puckette · 2018
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A variational inequality perspective on generative adversarial networks
G. Gidel, H. Berard, G. Vignoud, P. Vincent, and S. Lacoste-Julien · 2018
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Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2019
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Maximum entropy generators for energy-based models
R. Kumar, A. Goyal, A. Courville, and Y. Bengio · 2019
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A large-scale study on regularization and normalization in gans
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Mode seeking generative adversarial networks for diverse image synthesis, 2019
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Open questions about generative adversarial networks
A. Odena · 2019
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Small-gan: Speeding up gan training using core-sets
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Bridging the gap between f f -gans and wasserstein gans
J. Song and S. Ermon · 2019
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Logan: Latent optimisation for generative adversarial networks
Y. Wu, J. Donahue, D. Balduzzi, K. Simonyan, and T. Lillicrap · 2019
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Towards gan benchmarks which require generalization
I. Gulrajani, C. Raffel, and L. Metz · 2020
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Consistency regularization for generative adversarial networks
H. Zhang, Z. Zhang, A. Odena, and H. Lee · 2020
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Improved consistency regularization for gans
Z. Zhao, S. Singh, H. Lee, Z. Zhang, A. Odena, and H. Zhang · 2020
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Image augmentations for gan training
Z. Zhao, Z. Zhang, T. Chen, S. Singh, and H. Zhang · 2020
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