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We investigate under and overfitting in Generative Adversarial Networks (GANs), using discriminators unseen by the generator to measure generalization.
A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
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Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Do gans actually learn the distribution? an empirical study
Arora, S. and Zhang, Y · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 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
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
Cited alongside, same era.
The numerics of gans
Mescheder, L., Nowozin, S., and Geiger, A · 2017
Cited alongside, same era.
Gradient descent gan optimization is locally stable
Nagarajan, V. and Kolter, J. Z · 2017
Cited alongside, same era.
Approximability of discriminators implies diversity in gans
Bai, Y., Ma, T., and Risteski, A · 2018
Cited alongside, same era.
Finding mixed nash equilibria of generative adversarial networks
Hsieh, Y.-P., Liu, C., and Cevher, V · 2018
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2018
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The gan landscape: Losses, architectures, regularization, and normalization
Kurach, K., Lucic, M., Zhai, X., Michalski, M., and Gelly, S · 2018
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Barratt, S. and Sharma, R · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
Cited alongside, same era.
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Non-convex min-max optimization: Provable algorithms and applications in machine learning
Rafique, H., Liu, M., Lin, Q., and Yang, T · 2018
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