Talk:Variational autoencoder

Hi, the article is really interesting and well detailed, I believe it will be a really helpful starting point for those who are willing to study this topic. I just fixed some minor things, like a missing comma or repleced a term with a synonim. It would be nice if you could add a paragraph with some applications of this neural network :) --Lavalec (talk) 14:00, 18 June 2021 (UTC)

Hi, I confirm that the article is interesting and detailed. I'm not expert in this field, but I understood the basic things. --Beatrice Lotesoriere (talk) 14:32, 18 June 2021 (UTC)Beatrice Lotesoriere

Very well written article. I just made some minor language changes in a few sections. The only thing I would probably do, I would add some citations in the formulation section. --Wario93 (talk) 15:40, 18 June 2021 (UTC)

Good article, but I had to get rid of a bunch of unnecessary fluff in the Architecture section which obscured the point (diff : https://en.wikipedia.org/w/index.php?title=Variational_autoencoder&type=revision&diff=1040705234&oldid=1039806485 ). 26 August 2021

I disagree, the article really needs attention, it is very hard to understand the "Formulation" part now. I propose the following changes for the first paragraphs, but subsequent ones need revision as well:

From a formal perspective, given an input dataset vector $\mathbf {x}$ characterized by from an unknown probability function distribution $P(\mathbf {x} )$ and a multivariate latent encoding vector $\mathbf {z}$ , the objective is to model the data $P(\mathbf {x} )$ as a parametric distribution with density $p_{\theta }(\mathbf {x} )$, where $\theta$ is a vector of parameters to be learned. defined as the set of the network parameters.

For the parametric model we assume that each $\mathbf {x}$ is associated with (arises from) a latent encoding vector $\mathbf {z}$, and we write $p_{\theta }(\mathbf {x} ,\mathbf {z} )$ to denote their joint density.

It is possible to formalize this distribution as We can then write

$p_{\theta }(\mathbf {x} )=\int _{\mathbf {z} }p_{\theta }(\mathbf {x,z} )\,d\mathbf {z}$

where $p_{\theta }$ is the evidence of the model's data with marginalization performed over unobserved variables and thus $p_{\theta }(\mathbf {x,z} )$ represents the joint distribution between input data and its latent representation according to the network parameters $\theta$.

193.219.95.139 (talk) 10:18, 2 October 2021 (UTC)

Observations and suggestions for improvements

The following observations and suggestions for improvements were collected, following expert review of the article within the Science, Tecnology, Society and Wikipedia course at the Politecnico di Milano, in June 2021.

"Minor corrections:

- single layer perceptron => single-layer perceptron

- higher level representations => higher-level representations

- applied with => applied to

- composed by => composed of

- Information retrieval benefits => convoluted sentence

- modelling the relation between => modelling the relationship between

- predicting popularity => predicting the popularity"

Ettmajor (talk) 10:06, 11 July 2021 (UTC)

Does the prior $p(z)$ depend on $\theta$ or not?

In a vanilla Gaussian VAE, the prior follows a standard Gaussian with zero mean and unit variance, i.e., there is no parametrization ($\theta$ or whatsoever) concerning the prior $p(z)$ of the latent representations. On the other hand, the article as well as [Kingma&Welling2014] both parametrize the prior $p_{\theta }(z)$ with $\theta$, just as the likelihood $p_{\theta }(x\mid z)$. Clearly, the latter makes sense, since it is the very goal to learn $\theta$ through the probabilistic decoder as generative model for the likelihood $p_{\theta }(x\mid z)$. So is there a deeper meaning or sense in parametrizing the prior as $p_{\theta }(z)$ as well, with the very same parameters $\theta$ as the likelihood, or is it in fact a typo/mistake? — Preceding unsigned comment added by 46.223.162.38 (talk) 22:11, 11 October 2021 (UTC)

The image shows just a normal encoder

There is an image with a caption saying it is a variational autoencoder, but it is showing just a plain autoencoder.

In a different section, there is something described as a "trick", which seems to be the central point that distinguishes autoencoders from variational autoencoders.

I'm not sure that image should just be removed, or whether it make sense in the section anyway. Volker Siegel (talk) 14:18, 24 January 2022 (UTC)