Vanilla Variational Auto Encoder

[markusMM]

Hello WiseOdd,

I have recognise you vanilla VAE, which seems pretty neat despite of the fact that does not work as I remember a Gassian noise sparse model would work.

I have recently read 1606.05908 where VAE are explained quite good more or less. Threre is the PDF for X described as expectation value over $P(Z)$. Now, when you update your parameters of the Gaussian, normally the variance is a diagonal matrix of $\sigma^2$. So, of cause one could think about a variance for each point in the dimensionality of X, but I am not qute sure that is the proper common idea behind the variance of the Gaussian distribution in generative models.

$P(X) = \frac{1}{D_z} \sum_{\forall z \in Z} P(X|Z, \Theta) P(Z)$ using this annotation, the expectation for $\sigma^2$ based on the data would be something similar to $\sum_{\forall n \in N} \braket{X-\mu(z;\Theta)|X-\mu(z;\Theta)}$, right?

Maybe you can take a look on that code. Because your variance seems to appear as a matrix instead of a value or set of values for the Gaussian noise model.

So, did you thought explicitly about a full covariance matrix or was it just trial and error in this case?

If trying out expectation value(s) for the variance, let me know, what your experience is.

regards, Markus