M. Arbel, D. J. Sutherland, M. Binkowski, and A. Gretton, On gradient regularizers for MMD GANs, In NeurIPS, 2018.

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

P. Bachman, An architecture for deep, hierarchical generative models, NeurIPS, 2016.

C. Bishop, Pattern recognition and machine learning, 2006.

A. Brock, J. Donahue, and K. Simonyan, Large scale GAN training for high fidelity natural image synthesis, ICLR, 2019.

L. Chen, S. Dai, Y. Pu, E. Zhou, C. Li et al., Symmetric variational autoencoder and connections to adversarial learning, In AISTATS, 2018.

X. Chen, D. Kingma, T. Salimans, Y. Duan, P. Dhariwal et al., Variational lossy autoencoder, 2017.

H. D. Vries, F. Strub, J. Mary, H. Larochelle, O. Pietquin et al., Modulating early visual processing by language, NeurIPS, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01648683

L. Dinh, J. Sohl-dickstein, and S. Bengio, Density estimation using Real NVP, ICLR, 2017.

J. Donahue, P. Krähenbühl, and T. Darrell, Adversarial feature learning, ICLR, 2017.

G. Dorta, S. Vicente, L. Agapito, N. Campbell, and I. Simpson, Structured uncertainty prediction networks, CVPR, 2018.

V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky et al., Adversarially learned inference, 2017.

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

M. Elfeki, C. Couprie, M. Riviere, and M. Elhoseiny, GDPP: Learning diverse generations using determinantal point processes, 2018.

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

A. Grover, M. Dhar, and S. Ermon, Flow-GAN: Combining maximum likelihood and adversarial learning in generative models, 2018.

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

I. Gulrajani, K. Kumar, F. Ahmed, A. A. Taiga, F. Visin et al., PixelVAE: A latent variable model for natural images, 2017.

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, NeurIPS, 2017.

J. Ho, X. Chen, A. Srinivas, Y. Duan, and P. Abbeel, Flow++: Improving flow-based generative models with variational dequantization and architecture design, ICML, 2019.

X. Hou, L. Shen, K. Sun, and G. Qiu, Deep feature consistent variational autoencoder, WACV, 2017.

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

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

D. Kingma and P. , Glow: Generative flow with invertible 1x1 convolutions, NeurIPS, 2018.

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

D. Kingma, T. Salimans, R. Józefowicz, X. Chen, I. Sutskever et al., Improving variational autoencoders with inverse autoregressive flow, NeurIPS, 2016.

A. Larsen, S. Sønderby, H. Larochelle, and O. Winther, Autoencoding beyond pixels using a learned similarity metric, ICML, 2016.

Y. Li, K. Swersky, and R. Zemel, Generative moment matching networks, ICML, 2015.

Z. Lin, A. Khetan, G. Fanti, and S. Oh, PacGAN: The power of two samples in generative adversarial networks, In NeurIPS, 2018.

O. Litany, A. Bronstein, M. Bronstein, and A. Makadia, Deformable shape completion with graph convolutional autoencoders, CVPR, 2018.

T. Lucas and J. Verbeek, Auxiliary guided autoregressive variational autoencoders, ECML, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01652881

T. Lucas, C. Tallec, Y. Ollivier, and J. Verbeek, Mixed batches and symmetric discriminators for GAN training, ICML, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01791126

A. Makhzani, J. Shlens, N. Jaitly, and I. Goodfellow, Adversarial autoencoders, ICLR, 2016.

M. Mathieu, C. Couprie, and Y. Lecun, Deep multi-scale video prediction beyond mean square error, ICLR, 2016.

J. Menick and N. Kalchbrenner, Generating high fidelity images with subscale pixel networks and multidimensional upscaling, ICLR, 2019.

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

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

P. Ramachandran, T. Paine, P. Khorrami, M. Babaeizadeh, S. Chang et al., Fast generation for convolutional autoregressive models, ICLR workshop, 2017.

D. Rezende and S. Mohamed, Variational inference with normalizing flows, ICML, 2015.

D. Rezende, S. Mohamed, and D. Wierstra, Stochastic backpropagation and approximate inference in deep generative models, ICML, 2014.

M. Rosca, B. Lakshminarayanan, D. Warde-farley, and S. Mohamed, Variational approaches for auto-encoding generative adversarial networks, 2017.

Y. Saatchi, A. Wilson, and G. Bayesian, NeurIPS, 2017.

M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly, Assessing generative models via precision and recall, NeurIPS, 2018.

T. Salimans and D. Kingma, Weight normalization: A simple reparameterization to accelerate training of deep neural networks, In NeurIPS, 2016.

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

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

T. Salimans, H. Zhang, A. Radford, and D. Metaxas, Improving gans using optimal transport, In ICLR, 2018.

K. Shmelkov, C. Schmid, and K. Alahari, How good is my GAN?, In ECCV, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01850447

C. Sønderby, T. Raiko, L. Maaløe, S. Sønderby, and O. Winther, Ladder variational autoencoders, NeurIPS, 2016.

C. Sønderby, J. Caballero, L. Theis, W. Shi, and F. Huszár, Amortised MAP inference for image super-resolution, 2017.

H. Thanh-tung, T. Tran, and S. Venkatesh, Improving generalization and stability of generative adversarial networks, ICLR, 2019.

D. Ulyanov, A. Vedaldi, and V. Lempitsky, It takes (only) two: Adversarial generator-encoder networks, In AAAI, 2018.

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

H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, Self-attention generative adversarial networks, ICML, 2019.