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A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models.
A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., and Smola, A. J · 2007
Earlier work this paper cites.
On the quantitative analysis of deep belief networks
Salakhutdinov, R. and Murray, I · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2013
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.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Jimenez Rezende, D., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
Earlier work this paper cites.
Semi-supervised learning with deep generative models
Kingma, D. P., Rezende, D. J., Mohamed, S., and Welling, M · 2014
Earlier work this paper cites.
Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
Earlier work this paper cites.
Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z · 2015
Cited alongside, same era.
Generative moment matching networks
Li, Y., Swersky, K., and Zemel, R · 2015
Cited alongside, same era.
Makhzani, A., Shlens, J., Jaitly, N., and Goodfellow, I · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Cited alongside, same era.
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D · 2016
Later among the works it cites.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
Later among the works it cites.
Wasserstein GAN
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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Inferring the latent structure of human decision-making from raw visual inputs
Li, Y., Song, J., and Ermon, S · 2017
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Cited alongside, same era.
Pixelvae: A latent variable model for natural images
Gulrajani, I., Kumar, K., Ahmed, F., Taiga, A. A., Visin, F., Vázquez, D., and Courville, A. C · 2016
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 · 2016
Cited alongside, same era.
Improving variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., and Welling, M · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al
Cited in the paper.
Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K
Cited in the paper.
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
Closest in time.
Improved variational autoencoders for text modeling using dilated convolutions
Yang, Z., Hu, Z., Salakhutdinov, R., and Berg-Kirkpatrick, T · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J., Park, T., Isola, P., and Efros, A. A · 2017
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