<p>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 <a href="https://youtu.be/yV698Fi2XzE" rel="nofollow noreferrer">presentation</a> with the loss and re-parametrization trick of Variational Autoencoder, might help the principal task of regression.</p>

<p>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.</p>

<p>Has anyone tried such an approach, is it worth the work?
Thank you</p>

<neural-networks><convolutional-neural-networks><unsupervised-learning><autoencoders><supervised-learning>
Variational Autoencoder task for better feature extraction
Florentin Alexandru Iftimie
# Variational Autoencoder task for better feature extraction

(Asked by: username_0 on 2019-04-17)

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](https://youtu.be/yV698Fi2XzE) 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

**Tags:** neural-networks, convolutional-neural-networks, unsupervised-learning, autoencoders, supervised-learning


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