# Variational Autoencoder task for better feature extraction

I have a CNN with the regression task of a single scalar. I was wondering if an additional task of reconstructing the image (used for learning visual concepts), seen in a DeepMind presentation with the loss and re-parametrization trick of Variational Autoencoder, might help the principal task of regression.

So you can imagine some convolutions with the role of feature extraction with some output X (let's say a vector of 256 values), that X goes into the VAE which computes Z and then the reconstructed image. And then the original regression task would take either X or Z in order to compute that scalar value.

Has anyone tried such an approach, is it worth the work? Thank you

I have not worked on this but I think I can give you a theoretical perspective of using VAE's. Regression is a Supervised Learning task and is basically a mapping from Input to Output where the Neural Net will approximate the function $$f(input) = output$$.
VAE's on the other hand are good for finding how a latent variable affects the output. For example, if you have a task of training on a persons facial emotions, and if your latent space contains 2 variables $$z_1$$ and $$z_2$$ then you might find varying $$z_1$$ varies the amount of smile on the face, while varying $$z_2$$ might give the amount of drooping of eyes. I suggest you check this video from Stanford at ~44:00 to see this actually happens or check this blog. So VAE's might have been useful if your output contained more features which would vary according to variations in latent variables, but a single scalar output can only tell you about the rate of effect on varying a latent variable.