On the Latent Space of Wasserstein Auto-Encoders

Representation learning: A review and new perspectives

Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal · 2013

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Generative adversarial nets

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014

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Auto-encoding variational Bayes

Kingma, D. P. and Welling, M · 2014

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Adversarial autoencoders

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f-GAN: Training generative neural samplers using variational divergence minimization

Nowozin, Sebastian, Cseke, Botond, and Tomioka, Ryota · 2016

Cited alongside, same era.

Wasserstein GAN

Original

Arjovsky, M., Chintala, S., and Bottou, L · 2017

Cited alongside, same era.

GANs trained by a two time-scale update rule converge to a nash equilibrium

Original

Heusel, Martin, Ramsauer, Hubert, Unterthiner, Thomas, Nessler, Bernhard, Klambauer, Günter, and Hochreiter, Sepp · 2017

Cited alongside, same era.

Beta-vae: Learning basic visual concepts with a constrained variational framework

Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017

Cited alongside, same era.

Building machines that learn and think like people

Lake, Brenden M, Ullman, Tomer D, Tenenbaum, Joshua B, and Gershman, Samuel J · 2017

Later among the works it cites.

Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks

Original

Mescheder, L., Nowozin, S., and Geiger, A · 2017

Later among the works it cites.

Demystifying MMD GANs

Binkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018

Closest in time.

Wasserstein auto-encoders

Original

Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2018

Closest in time.