<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>

CC BY-SA 3.0
2018-03-12T13:55:03.750Z
2018-03-12T13:55:03.750Z
hellmean
<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>

<neural-networks><deep-learning><deepmind><transformer>
What is regression layer in a spatial transformer?
nivter
# 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)


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