Variational Autoencoder with Learned Latent Structure

Diffusion variational autoencoders

Original

Rey, L. A. P., Menkovski, V., and Portegies, J. W. (2019) · 1901

Earlier work this paper cites.

Sparse coding with an overcomplete basis set: A strategy employed by v1?

Olshausen, B. A. and Field, D. J. (1997) · 1997

Earlier work this paper cites.

Gradient-based learning applied to document recognition

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al. (1998) · 1998

Earlier work this paper cites.

Nonlinear dimensionality reduction by locally linear embedding

Roweis, S. T. and Saul, L. K. (2000) · 2000

Earlier work this paper cites.

A global geometric framework for nonlinear dimensionality reduction

Tenenbaum, J. B., De Silva, V., and Langford, J. C. (2000) · 2000

Earlier work this paper cites.

Non-local manifold tangent learning

Bengio, Y. and Monperrus, M. (2005) · 2005

Earlier work this paper cites.

Learning to traverse image manifolds

Dollár, P., Rabaud, V., and Belongie, S. J. (2007) · 2007

Earlier work this paper cites.

Learning transport operators for image manifolds

Culpepper, B. J. and Olshausen, B. A. (2009) · 2009

Earlier work this paper cites.

Auto-encoding variational bayes

Kingma, D. P. and Welling, M. (2013) · 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) · 2014

Cited alongside, same era.

Stochastic backpropagation and approximate inference in deep generative models

Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014

Cited alongside, same era.

Importance weighted autoencoders

Original

Burda, Y., Grosse, R., and Salakhutdinov, R. (2015) · 2015

Cited alongside, same era.

Adam: A method for stochastic optimization

Kingma, D. P. and Ba, J. (2015) · 2015

Cited alongside, same era.

Testing the manifold hypothesis

Fefferman, C., Mitter, S., and Narayanan, H. (2016) · 2016

Cited alongside, same era.

Unsupervised representation learning with deep convolutional generative adversarial networks

Radford, A., Metz, L., and Chintala, S. (2016) · 2016

The riemannian geometry of deep generative models

Shao, H., Kumar, A., and Thomas Fletcher, P. (2018) · 2018

Later among the works it cites.

Vae with a vampprior

Original

Tomczak, J. and Welling, M. (2018) · 2018

Later among the works it cites.

Reparameterizing distributions on lie groups

Falorsi, L., de Haan, P., Davidson, T. R., and Forré, P. (2019) · 2019

Later among the works it cites.

Continuous hierarchical representations with poincaré variational auto-encoders

Mathieu, E., Le Lan, C., Maddison, C. J., Tomioka, R., and Teh, Y. W. (2019) · 2019

Later among the works it cites.

A wrapped normal distribution on hyperbolic space for gradient-based learning

Nagano, Y., Yamaguchi, S., Fujita, Y., and Koyama, M. (2019) · 2019

Later among the works it cites.

Representing closed transformation paths in encoded network latent space