Body: <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>
  
Tags: <neural-networks><convolutional-neural-networks><unsupervised-learning><autoencoders><supervised-learning>
Title: Variational Autoencoder task for better feature extraction
UserDisplayName: Florentin Alexandru Iftimie
ThreadText: # 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
  
  ---
  
  ## 0 answers
  
  