Answers.Body: <blockquote>
  <p>What happens to the size of output feature map in case of full convolution?</p>
  </blockquote>
  <p>It increases.</p>
  <p><a href="https://i.sstatic.net/0rs9l.gif" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0rs9l.gif" alt="enter image description here" /></a>
  First one is valid padding: the blue square is not padded, so the green square is smaller.  Third one is same padding: the blue square is padded just enough so that the green square is the same size.  Fourth one is full padding: the blue square is padded as much as possible for that size of filter, so the green square is larger.</p>
  <p>From <a href="https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480" rel="nofollow noreferrer">here</a>.</p>
  
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Answers.CreationDate: 2021-05-13T08:37:59.553Z
Answers.LastActivityDate: 2021-05-13T08:37:59.553Z
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Body: <p>In valid convolution, the <strong>size of the output shrinks</strong> at each layer. So after some point of time additional layers cannot meaningfully performs convolution. For this reason, same convolution is introduced, where where the <strong>size of the output remains intact</strong>. This is achieved by padding with enough number of zeroes at the borders of input image.</p>
  <p>What happens to the size of output feature map in case of full convolution?</p>
  <p>If it remains intact then what is the difference between same convolution and full convolution?</p>
  
Tags: <convolutional-neural-networks><convolution>
Title: What is the difference between same convolution and full convolution in terms of feature map size?
UserDisplayName: satya
ThreadText: # What is the difference between same convolution and full convolution in terms of feature map size?
  
  (Asked by: username_0 on 2021-05-13)
  
  In valid convolution, the **size of the output shrinks** at each layer. So after some point of time additional layers cannot meaningfully performs convolution. For this reason, same convolution is introduced, where where the **size of the output remains intact**. This is achieved by padding with enough number of zeroes at the borders of input image.
  
  What happens to the size of output feature map in case of full convolution?
  
  If it remains intact then what is the difference between same convolution and full convolution?
  
  **Tags:** convolutional-neural-networks, convolution
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-05-13. Score: 3
  
  > What happens to the size of output feature map in case of full convolution?
  
  It increases.
  
  [![enter image description here](https://i.sstatic.net/0rs9l.gif)](https://i.sstatic.net/0rs9l.gif)
  First one is valid padding: the blue square is not padded, so the green square is smaller. Third one is same padding: the blue square is padded just enough so that the green square is the same size. Fourth one is full padding: the blue square is padded as much as possible for that size of filter, so the green square is larger.
  
  From [here](https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480).
  
  
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