Answers.Body: <p>Basically, it means that the "localization network" should output a set of real valued parameters (typically 6 numbers). The word "regression" doesn't bear any specific meaning. </p>
  
  <p>Any network that relies on the original image as input (directly or indirectly) and outputs 6 numbers, would work. And its last layer would qualify as "regression layer" as long as it is not restricted in the real domain (not normalized, softmaxed, etc)</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-03-12T13:55:03.750Z
Answers.LastActivityDate: 2018-03-12T13:55:03.750Z
Answers.UserDisplayName: hellmean
Body: <p>I came across this line while reading the <a href="https://arxiv.org/abs/1506.02025" rel="nofollow noreferrer">original paper</a> on Spatial Transformers by Deepmind in the last paragraph of Sec 3.1:</p>
  
  <blockquote>
    <p>The localisation network function floc() can take any form, such as a fully-connected network or a convolutional network, but should include a final regression layer to produce the transformation parameters θ.</p>
  </blockquote>
  
  <p>I understand what regression is, but what is meant by a regression layer?</p>
  
Tags: <neural-networks><deep-learning><deepmind><transformer>
Title: What is regression layer in a spatial transformer?
UserDisplayName: nivter
ThreadText: # What is regression layer in a spatial transformer?
  
  (Asked by: username_0 on 2018-03-11)
  
  I came across this line while reading the [original paper](https://arxiv.org/abs/1506.02025) on Spatial Transformers by Deepmind in the last paragraph of Sec 3.1:
  
  > The localisation network function floc() can take any form, such as a fully-connected network or a convolutional network, but should include a final regression layer to produce the transformation parameters θ.
  
  I understand what regression is, but what is meant by a regression layer?
  
  **Tags:** neural-networks, deep-learning, deepmind, transformer
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-03-12. Score: 1 | Accepted answer
  
  Basically, it means that the "localization network" should output a set of real valued parameters (typically 6 numbers). The word "regression" doesn't bear any specific meaning.
  
  Any network that relies on the original image as input (directly or indirectly) and outputs 6 numbers, would work. And its last layer would qualify as "regression layer" as long as it is not restricted in the real domain (not normalized, softmaxed, etc)
  
  
  ---
  
  