Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
Feedforward neural networks trained to reconstruct their own input. Usually one of the hidden layers is a "bottleneck", leading to encoder->decoder interpretation.
21
votes
2
answers
20k
views
Why binary crossentropy can be used as the loss function in autoencoders? [duplicate]
I was wondering why binary crossentropy can be used as the loss function in autoencoders trained on (normalized) images, e.g. here or this paper? …
11
votes
Accepted
Why binary crossentropy can be used as the loss function in autoencoders?
That's simply wrong since in most of the machine learning models (including autoencoders) we are trying to minimize a loss/cost function. …