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Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M. W · 2010
Earlier work this paper cites.
cuDNN: Efficient primitives for deep learning
Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., and Shelhamer, E · 2014
Earlier work this paper cites.
Generative Adversarial Nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Denton, E. L., Chintala, S., Szlam, A., and Fergus, R · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
Earlier work this paper cites.
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
Earlier work this paper cites.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
Earlier work this paper cites.
Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
Wasserstein Generative Adversarial Networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Earlier work this paper cites.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
Cited alongside, same era.
On convergence and stability of GANs
Kodali, N., Abernethy, J., Hays, J., and Kira, Z · 2017
Cited alongside, same era.
Lim, J. H. and Ye, J. C · 2017
Cited alongside, same era.
Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Smolley, S. P · 2017
Cited alongside, same era.
Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
Closest in time.
Winner’s Curse? On Pace, Progress, and Empirical Rigor, 2018
Sculley, D., Snoek, J., Wiltschko, A., and Rahimi, A · 2018
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Deep generative models for distribution-preserving lossy compression
Tschannen, M., Agustsson, E., and Lucic, M · 2018
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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Unterthiner, T., Nessler, B., Seward, C., Klambauer, G., Heusel, M., Ramsauer, H., and Hochreiter, S · 2018
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Pros and cons of GAN evaluation measures
Borji, A · 2019
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Roth, K., Lucchi, A., Nowozin, S., and Hofmann, T · 2017
Cited alongside, same era.
Demystifying MMD GANs
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
Cited alongside, same era.
Self-Supervised Generative Adversarial Networks
Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N · 2018
Cited alongside, same era.
Many paths to equilibrium: GANs do not need to decrease a divergence at every step
Fedus, W., Rosca, M., Lakshminarayanan, B., Dai, A. M., Mohamed, S., and Goodfellow, I · 2018
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Cited alongside, same era.
Are GANs Created Equal? A Large-Scale Study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O · 2018
Cited alongside, same era.
Which training methods for GANs do actually Converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
Cited alongside, same era.
On Self Modulation for Generative Adversarial Networks
Chen, T., Lucic, M., Houlsby, N., and Gelly, S · 2019
Closest in time.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Closest in time.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
Closest in time.
High-Fidelity Image Generation With Fewer Labels
Lucic, M., Tschannen, M., Ritter, M., Zhai, X., Bachem, O., and Gelly, S · 2019
Closest in time.
Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Menick, J. and Kalchbrenner, N · 2019
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Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A · 2019
Closest in time.