Body: <p>I am trying to implement a paper on Image tempering detection and localization, the paper is <a href="https://ieeexplore.ieee.org/document/8554259" rel="nofollow noreferrer">Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks</a>, I was able to implement the SCNN, the one surrounded by red dots, I could not quite understand the FCNN, the one that is surrounded with blue dots.</p>
  <p>The problem I am facing is: How the network made features vector from (1048 x 100) to (523 x 100) through max-pooling (instead of 524 x 100), and from (523 x 100) to (260 x 100) and then (260 x 100) to (256, ).</p>
  <p><a href="https://i.sstatic.net/Dz3hl.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Dz3hl.png" alt="enter image description here" /></a></p>
  <p>It appears that the given network diagram might be wrong, but, if it is wrong, how could it be published in IEEE. Please, help me understand how the FCNN is constructed.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-20T04:26:10.263Z
Comments.Text: How did you get 520x100? With a max pooling filter of 2x1 I would assume the output should be 524x100
Comments.UserDisplayName: Recessive
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-20T04:34:30.313Z
Comments.Text: @Recessive sorry, typo it is 524 x 100
Comments.UserDisplayName: kiran
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-20T04:42:28.827Z
Comments.Text: Ok that makes sense, at least the clarifies. Unfortunately I don't know why their numbers are off by one. I can only assume that they shifted the max pool filter by 1 at some point, but I can't know for sure or why. Hopefully someone else does
Comments.UserDisplayName: Recessive
Tags: <neural-networks><convolutional-neural-networks><image-segmentation><convolutional-layers><pooling>
Title: How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?
UserDisplayName: kiran
ThreadText: # How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?
  
  (Asked by: username_0 on 2020-07-20)
  
  I am trying to implement a paper on Image tempering detection and localization, the paper is [Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks](https://ieeexplore.ieee.org/document/8554259), I was able to implement the SCNN, the one surrounded by red dots, I could not quite understand the FCNN, the one that is surrounded with blue dots.
  
  The problem I am facing is: How the network made features vector from (1048 x 100) to (523 x 100) through max-pooling (instead of 524 x 100), and from (523 x 100) to (260 x 100) and then (260 x 100) to (256, ).
  
  [![enter image description here](https://i.sstatic.net/Dz3hl.png)](https://i.sstatic.net/Dz3hl.png)
  
  It appears that the given network diagram might be wrong, but, if it is wrong, how could it be published in IEEE. Please, help me understand how the FCNN is constructed.
  
  **Tags:** neural-networks, convolutional-neural-networks, image-segmentation, convolutional-layers, pooling
  
  > **Comments:**
  > **username_1**: How did you get 520x100? With a max pooling filter of 2x1 I would assume the output should be 524x100  
  > **username_0**: @username_1 sorry, typo it is 524 x 100  
  > **username_1**: Ok that makes sense, at least the clarifies. Unfortunately I don't know why their numbers are off by one. I can only assume that they shifted the max pool filter by 1 at some point, but I can't know for sure or why. Hopefully someone else does  
  
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  ## 0 answers
  
  