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Results tagged with variational-autoencoder
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For questions related to variational auto-encoders (VAEs). The first VAE was proposed in "Auto-Encoding Variational Bayes" (2013) by Diederik P. Kingma and Max Welling. There are several other VAEs, for example, the conditional VAE.
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What are the roles of the prior $\mathrm{p}(\mathbf{z})$ in a VAE?
I know the encoder is variational posterior $q_{\phi}(\mathbf{z} \mid \mathbf{x})$.
I also know that the decoder represents the likelihood:
$p_{\theta}(\mathbf{x} \mid \mathbf{z})$.
My question is abo …
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Why do we use $q_{\phi}(z \mid x^{(i)})$ in the objective function of amortized variational ...
In page 21 here, it states:
General Idea of Amortization: if same inference problem needs to be solved many times, can we parameterize a neural network to solve it?
Our case: for all $x^{(i)}$ we wan …
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Are the authors of the VAE paper writing the PDFs as a function of the random variables?
Usually, I see the conventions:
discrete random variable is denoted as $X$,
the pmf is written as $P(X=x)$ or $p(X=x)$ or $p_{X}(x)$ or $p(x)$, where $x$ is an instance of $X$
a continuous random var …
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For the VAE, should the input, output and latent variable code be random variables?
For a variational autoencoder, we have input $x$ (assume 1 data point for now, like an image), a latent code sampled from the decoder, $z$, and an output $\hat{x}$.
If I were to draw a diagram for the …
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Do we use two distinct layers to compute the mean and variance of a Gaussian encoder/decoder...
I am looking at appendix C of the VAE paper:
It says:
C.1 Bernoulli MLP as decoder
In this case let $p_{\boldsymbol{\theta}}(\mathbf{x} \mid \mathbf{z})$ be a multivariate Bernoulli whose probabiliti …
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How is the VAE related to the Autoencoding Variational Bayes (AEVB) algorithm?
I am familiar with the variational autoencoder, but not totally clear on what exactly the AEVB is.
In the original VAE paper (by Kingma and Welling), he uses both the terms variational autoencoder and …
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How does the VAE learn a joint distribution?
I found the following paragraph from An Introduction to
Variational Autoencoders sounds relevant, but I am not fully understanding it.
A VAE learns stochastic mappings between an observed $\mathbf{x} …
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