Body: <p>Consider the following excerpt from <code>abstract</code> of the research paper titled Better <a href="http://proceedings.mlr.press/v28/bengio13.pdf" rel="nofollow noreferrer">Mixing via Deep Representations</a> by Yoshua Bengio et al.</p>
  <blockquote>
  <p>It has been hypothesized, and supported with experimental evidence,
  that deeper representations, when well trained, tend to do a <strong>better
  job at disentangling the underlying factors of variation</strong>.</p>
  </blockquote>
  <p>In general, as per my current knowledge, we want to preserve the factors that contribute to variation in the final representation. But, the abstract is contrary to that. Where am I going wrong? Why there is a need to disentangle the factors of variation?</p>
  
Tags: <datasets><papers><features><representation-learning>
Title: Why disentangling the features of variation in representation?
UserDisplayName: hanugm
ThreadText: # Why disentangling the features of variation in representation?
  
  (Asked by: username_0 on 2021-09-04)
  
  Consider the following excerpt from `abstract` of the research paper titled Better [Mixing via Deep Representations](http://proceedings.mlr.press/v28/bengio13.pdf) by Yoshua Bengio et al.
  
  > It has been hypothesized, and supported with experimental evidence,
  > that deeper representations, when well trained, tend to do a **better
  > job at disentangling the underlying factors of variation**.
  
  In general, as per my current knowledge, we want to preserve the factors that contribute to variation in the final representation. But, the abstract is contrary to that. Where am I going wrong? Why there is a need to disentangle the factors of variation?
  
  **Tags:** datasets, papers, features, representation-learning
  
  ---
  
  ## 0 answers
  
  