, https://github.com/bioinf-jku/TTUR 3. https://github.com/tkarras/progressive_growing_of_gans 4. Source code. http://thoth.inrialpes.fr/research/ganeval 5. Supplementary material, also available in arXiv technical report at https

M. Arjovsky and L. Bottou, Towards principled methods for training generative adversarial networks, p.ICLR, 2017.

M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, p.ICML, 2017.

S. Barratt and R. Sharma, A note on the Inception score. arXiv preprint, 2018.

D. Berthelot, T. Schumm, and L. Metz, BEGAN: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv, pp.1703-10717, 2017.

K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2017.
DOI : 10.1109/CVPR.2017.18

F. Calimeri, A. Marzullo, C. Stamile, and G. Terracina, Biomedical Data Augmentation Using Generative Adversarial Neural Networks, In: ICANN, vol.115, issue.3, 2017.
DOI : 10.1007/978-3-319-46630-9_13

X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever et al., Info- GAN: Interpretable representation learning by information maximizing generative adversarial nets, p.NIPS, 2016.

Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semisupervised learning that requires a bad GAN, p.NIPS, 2017.

J. Deng, W. Dong, R. Socher, L. J. Li, K. Li et al., ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, p.CVPR, 2009.
DOI : 10.1109/CVPR.2009.5206848

E. L. Denton, S. Chintala, A. Szlam, and R. Fergus, Deep generative image models using a laplacian pyramid of adversarial networks, p.NIPS, 2015.

V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb et al., Adversarially learned inference, p.ICLR, 2017.

V. Dumoulin, J. Shlens, and M. Kudlur, A learned representation for artistic style, p.ICLR, 2017.

M. Frid-adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, Synthetic data augmentation using GAN for improved liver lesion classification, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), p.ISBI, 2018.
DOI : 10.1109/ISBI.2018.8363576

URL : http://arxiv.org/pdf/1801.02385

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, p.NIPS, 2014.

I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, Improved training of Wasserstein GANs, p.NIPS, 2017.

K. He, X. Zhang, S. Ren, and J. Sun, Identity Mappings in Deep Residual Networks, p.ECCV, 2016.
DOI : 10.1109/CVPR.2015.7298594

M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, GANs trained by a two time-scale update rule converge to a local Nash equilibrium, p.NIPS, 2017.

T. K. Ho, Random decision forests, In: ICDAR, 1995.

P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, Image-to-Image Translation with Conditional Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2017.
DOI : 10.1109/CVPR.2017.632

URL : http://arxiv.org/pdf/1611.07004

T. Karras, T. Aila, S. Laine, and J. Lehtinen, Progressive growing of GANs for improved quality, stability, and variation, p.ICLR, 2018.

D. Kingma and J. Ba, Adam: A method for stochastic optimization, p.ICLR, 2015.

D. P. Kingma and M. Welling, Auto-encoding variational Bayes, p.ICLR, 2014.

A. Krizhevsky, Learning multiple layers of features from tiny images, Tech. rep, 2009.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6, p.NIPS, 2012.
DOI : 10.1162/neco.2009.10-08-881

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham et al., Photo-realistic single image superresolution using a generative adversarial network, p.CVPR, 2017.
DOI : 10.1109/cvpr.2017.19

URL : http://arxiv.org/pdf/1609.04802

M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet, Are GANs created equal? A large-scale study. arXiv preprint arXiv, pp.1711-10337, 2017.

L. Van-der-maaten and G. Hinton, Visualizing data using t-SNE, JMLR, vol.9, pp.2579-2605, 2008.

X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang et al., Least Squares Generative Adversarial Networks, 2017 IEEE International Conference on Computer Vision (ICCV), p.ICCV, 2017.
DOI : 10.1109/ICCV.2017.304

URL : http://arxiv.org/pdf/1611.04076

M. Mirza and S. Osindero, Conditional generative adversarial nets. arXiv preprint arXiv:1411, p.1784, 2014.

T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, Spectral normalization for generative adversarial networks, p.ICLR, 2018.

T. Miyato and M. Koyama, cGANs with projection discriminator, In: ICLR, 2018.

M. Molano-mazon, A. Onken, E. Piasini, and S. Panzeri, Synthesizing realistic neural population activity patterns using generative adversarial networks, p.ICLR, 2018.

L. Mosser, O. Dubrule, and M. J. Blunt, Reconstruction of three-dimensional porous media using generative adversarial neural networks, Physical Review E, vol.1294, issue.4, p.43309, 2017.
DOI : 10.1093/mnrasl/slx008

S. Nowozin, B. Cseke, and R. Tomioka, f-GAN: Training generative neural samplers using variational divergence minimization, p.NIPS, 2016.

A. Odena, C. Olah, and J. Shlens, Conditional image synthesis with auxiliary classifier GANs, p.ICML, 2017.

M. Paganini, L. De-oliveira, and B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters, Physical Review Letters, vol.8, issue.4, p.42003, 2018.
DOI : 10.1088/1674-1137/38/9/090001

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

J. Rabin, G. Peyré, J. Delon, and M. Bernot, Wasserstein Barycenter and Its Application to Texture Mixing, Intl. Conf. Scale Space and Variational Methods in Computer Vision, 2011.
DOI : 10.1109/18.119725

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, p.ICLR, 2016.

T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford et al., Improved techniques for training GANs, p.NIPS, 2016.

T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma, PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications, p.ICLR, 2017.

H. Shin, J. K. Lee, J. Kim, and J. Kim, Continual learning with deep generative replay, p.NIPS, 2017.

A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang et al., Learning from Simulated and Unsupervised Images through Adversarial Training, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2017.
DOI : 10.1109/CVPR.2017.241

URL : http://arxiv.org/pdf/1612.07828

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.CVPR, 2015.
DOI : 10.1109/CVPR.2015.7298594

L. Theis, A. Oord, and M. Bethge, A note on the evaluation of generative models, p.ICLR, 2016.

T. Tran, T. Pham, G. Carneiro, L. Palmer, and I. Reid, A Bayesian data augmentation approach for learning deep models, p.NIPS, 2017.

A. Van-den-oord, N. Kalchbrenner, and K. Kavukcuoglu, Pixel recurrent neural networks, In: ICML, 2016.

Y. X. Wang, R. Girshick, M. Hebert, and B. Hariharan, Low-shot learning from imaginary data, p.CVPR, 2018.
DOI : 10.1109/cvpr.2018.00760

URL : http://arxiv.org/pdf/1801.05401

K. Yun, J. Bustos, and T. Lu, Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks, Electronic Imaging, vol.2018, issue.9, 2018.
DOI : 10.2352/ISSN.2470-1173.2018.09.SRV-127

URL : http://arxiv.org/pdf/1801.09804

H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang et al., Stack- GAN: Text to photo-realistic image synthesis with stacked generative adversarial networks, p.ICCV, 2017.

J. Zhao, M. Mathieu, and Y. Lecun, Energy-based generative adversarial networks, p.ICLR, 2017.

Z. Zhong, L. Zheng, Z. Zheng, S. Li, and Y. Yang, Camera style adaptation for person re-identification, p.CVPR, 2018.

J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, 2017 IEEE International Conference on Computer Vision (ICCV), p.ICCV, 2017.
DOI : 10.1109/ICCV.2017.244