A. A. Alemi, B. Poole, I. Fischer, J. V. Dillon, R. A. Saurous et al., Fixing a Broken ELBO, 2017.

M. Arjovsky and L. Bottou, Towards Principled Methods for Training Generative Adversarial Networks. International Conference on Learning Representations, 2017.

A. Dosovitskiy and T. Brox, Generating Images with Perceptual Similarity Metrics based on Deep Networks, Advances in Neural Information Processing Systems, vol.29, pp.658-666, 2016.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative Adversarial Nets, Advances in Neural Information Processing Systems, vol.27, pp.2672-2680, 2014.

I. Gulrajani, K. Kumar, F. Ahmed, A. A. Taïga, F. Visin et al., Pix-elVAE: A Latent Variable Model for Natural Images, ArXiv, 2016.

J. He, D. Spokoyny, G. Neubig, and T. Berg-kirkpatrick, Lagging Inference Networks and Posterior Collapse in Variational Autoencoders, ArXiv, 2019.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1063-6919, 2016.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot et al., Shakir Mohamed, and Alexander Lerchner. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, vol.ICLR, p.6, 2017.

C. Huang, S. Tan, A. Lacoste, and A. Courville, Improving Explorability in Variational Inference with Annealed Variational Objectives, Advances in Neural Information Processing Systems 31, pp.9701-9711, 2018.

H. Huang, R. He, Z. Sun, and T. Tan, IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis, Advances in Neural Information Processing Systems, vol.31, pp.52-63, 2018.

P. Diederik, M. Kingma, and . Welling, Auto-Encoding Variational Bayes, International Conference on Learning Representations, 2014.

S. Durk-p-kingma, D. Mohamed, M. Jimenez-rezende, and . Welling, Semisupervised Learning with Deep Generative Models, Advances in Neural Information Processing Systems, vol.27, pp.3581-3589, 2014.

P. Durk, T. Kingma, R. Salimans, X. Jozefowicz, I. Chen et al., Improved Variational Inference with Inverse Autoregressive Flow, Advances in Neural Information Processing Systems, vol.29, pp.4743-4751, 2016.

A. Boesen-lindbo-larsen, S. K. Sønderby, H. Larochelle, and O. Winther, Autoencoding Beyond Pixels Using a Learned Similarity Metric, Proceedings of the 33rd International Conference on International Conference on Machine Learning, vol.48, pp.1558-1566, 2016.

Z. Liu, P. Luo, X. Wang, and X. Tang, Deep Learning Face Attributes in the Wild, Proceedings of International Conference on Computer Vision (ICCV), pp.De- cember, 2015.

G. Loaiza, -. Ganem, and J. P. , Cunningham. The continuous Bernoulli: fixing a pervasive error in variational autoencoders, Conference on Neural Information Processing Systems, 2019.

J. Lucas, G. Tucker, B. Roger, M. Grosse, and . Norouzi, Don\textquotesingle t Blame the ELBO! A Linear VAE Perspective on Posterior Collapse, Advances in Neural Information Processing Systems, vol.32, pp.9403-9413, 2019.

E. Mathieu, C. L. Lan, C. J. Maddison, R. Tomioka, and Y. Teh, Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders, 2019.

Y. Nagano, S. Yamaguchi, Y. Fujita, and M. Koyama, A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning, ICML, 2019.

E. Nalisnick and P. Smyth, Stickbreaking variational autoencoders, International Conference on Learning Representations (ICLR, 2017.

A. Radford, L. Metz, and S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015.

A. Razavi, A. Van-den-oord, and O. Vinyals, Generating Diverse High-Fidelity Images with VQ-VAE-2, Advances in Neural Information Processing Systems, vol.32, pp.14837-14847, 2019.

D. Jimenez-rezende, S. Mohamed, and D. Wierstra, Stochastic Backpropagation and Approximate Inference in Deep Generative Models, International Conference on Learning Representations, 2014.

H. Sadeghi, E. Andriyash, W. Vinci, L. Buffoni, and M. H. Amin, PixelVAE++: Improved PixelVAE with Discrete Prior. ArXiv, 2019.

T. Casper-kaae-sønderby, L. Raiko, . Maaløe, O. Søren-kaae-sønderby, and . Winther, Ladder Variational Autoencoders, Advances in Neural Information Processing Systems, vol.29, pp.3738-3746, 2016.

L. Theis, Aäron van den Oord, and Matthias Bethge. A note on the evaluation of generative models. International Conference on Learning Representations, 2016.

A. Van-den-oord, O. Vinyals, K. Kavukcuoglu, ;. I. Guyon, U. V. Luxburg et al., Neural Discrete Representation Learning, Advances in Neural Information Processing Systems, vol.30, pp.6306-6315, 2017.