From Variational to Deterministic Autoencoders: Difference between revisions
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(Created page with "== Presented by == Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Scholkopf == Introduction == == Previous Work == == Motivation == ==...") |
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Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Scholkopf | Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Scholkopf | ||
== Introduction == | == Introduction == | ||
This paper presents an alternative framework for generative modeling that is deterministic. | |||
They suggest that sampling from a stochastic encoder within a VAE can be interpreted as injecting noise into the input of a deterministic decoder and propose a framework for a regularized deterministic autoencoder (RAE) to generate samples that are comparable or better than those produced by VAE's. | |||
== Previous Work == | == Previous Work == | ||
Revision as of 01:07, 31 October 2020
Presented by
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Scholkopf
Introduction
This paper presents an alternative framework for generative modeling that is deterministic. They suggest that sampling from a stochastic encoder within a VAE can be interpreted as injecting noise into the input of a deterministic decoder and propose a framework for a regularized deterministic autoencoder (RAE) to generate samples that are comparable or better than those produced by VAE's.