<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>

<datasets><papers><features><representation-learning>
Why disentangling the features of variation in representation?
hanugm
# 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


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## 0 answers

