Answers.Body: <p>The channel they are talking about is the depth of the layer L.</p>
  
  <p><a href="https://i.sstatic.net/bM582.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/bM582.jpg" alt="enter image description here"></a></p>
  
  <p>In this image, the channel is 5. There are 5 $3*2$ filters</p>
  
  <p>They optimize an image from noise, to better respond to the filter F, in the layer L. Then you obtain this kind of image, and you can try to interpret the prupose of that filter (it learns to detect eyes, faces, wheels, and so on)</p>
  
  <p>EDIT: To be more precise, you visualise the channel only if you optimise images for every filters, otherwise, you obtain a filter visualisation (if you do the optimisation one time, for one filter)</p>
  
  <p>They call it "channel", because in a colored image, you have 3 dimensions : witdh, height, and channel (for color), as layers which have also 3 dimensions.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-29T08:24:56.330Z
Answers.LastActivityDate: 2018-08-29T16:55:14.630Z
Answers.LastEditDate: 2018-08-29T16:55:14.630Z
Answers.UserDisplayName: Jérémy Blain
Body: <p>Tensorflow/Lucid is able to visualize what a "channel" of a layer of a neural network (image recognition, Inception-v1) responds to. Even after studying the tutorial, the source code, the three research papers on lucid and comments by the authors on Hacker News, I'm still not clear on how "channels" are supposed to be defined and individuated. Can somebody shed some light on this? Thank you.</p>
  
  <p><a href="https://github.com/tensorflow/lucid" rel="nofollow noreferrer">https://github.com/tensorflow/lucid</a> <br/>
  <a href="https://news.ycombinator.com/item?id=15649456" rel="nofollow noreferrer">https://news.ycombinator.com/item?id=15649456</a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-08-29T08:04:36.117Z
Comments.Text: I guess they're talking about filter. They optimize an image to make a high respond on the filter (channel)
Comments.UserDisplayName: Jérémy Blain
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-08-29T08:06:04.653Z
Comments.Text: (I don't read the papers, I don't see them on the github page. Moreover, they don't seem to talk about channel on the github page you've linked)
Comments.UserDisplayName: Jérémy Blain
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-08-29T08:08:50.517Z
Comments.Text: They talk about them in the tutorial and the linked articles. https://colab.research.google.com/github/tensorflow/lucid/blob/master/notebooks/tutorial.ipynb https://distill.pub/2018/building-blocks/ https://distill.pub/2017/feature-visualization/
Comments.UserDisplayName: Jonas
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-08-29T08:09:49.600Z
Comments.Text: Ohh, my bad, I didn't see there are two links ^^" (I edited your post to better see the two links)
Comments.UserDisplayName: Jérémy Blain
Tags: <tensorflow><computer-vision>
Title: What exactly does "channel" refer to in tensorflow/lucid?
UserDisplayName: Jonas
ThreadText: # What exactly does "channel" refer to in tensorflow/lucid?
  
  (Asked by: username_0 on 2018-08-29)
  
  Tensorflow/Lucid is able to visualize what a "channel" of a layer of a neural network (image recognition, Inception-v1) responds to. Even after studying the tutorial, the source code, the three research papers on lucid and comments by the authors on Hacker News, I'm still not clear on how "channels" are supposed to be defined and individuated. Can somebody shed some light on this? Thank you.
  
  <https://github.com/tensorflow/lucid>   
  <https://news.ycombinator.com/item?id=15649456>
  
  **Tags:** tensorflow, computer-vision
  > **Comments:**
  > **username_1**: I guess they're talking about filter. They optimize an image to make a high respond on the filter (channel)  
  > **username_1**: (I don't read the papers, I don't see them on the github page. Moreover, they don't seem to talk about channel on the github page you've linked)  
  > **username_0**: They talk about them in the tutorial and the linked articles. https://colab.research.google.com/github/tensorflow/lucid/blob/master/notebooks/tutorial.ipynb https://distill.pub/2018/building-blocks/ https://distill.pub/2017/feature-visualization/  
  > **username_1**: Ohh, my bad, I didn't see there are two links ^^" (I edited your post to better see the two links)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-08-29. Score: 3 | Accepted answer
  
  The channel they are talking about is the depth of the layer L.
  
  [![enter image description here](https://i.sstatic.net/bM582.jpg)](https://i.sstatic.net/bM582.jpg)
  
  In this image, the channel is 5. There are 5 $3\*2$ filters
  
  They optimize an image from noise, to better respond to the filter F, in the layer L. Then you obtain this kind of image, and you can try to interpret the prupose of that filter (it learns to detect eyes, faces, wheels, and so on)
  
  EDIT: To be more precise, you visualise the channel only if you optimise images for every filters, otherwise, you obtain a filter visualisation (if you do the optimisation one time, for one filter)
  
  They call it "channel", because in a colored image, you have 3 dimensions : witdh, height, and channel (for color), as layers which have also 3 dimensions.
  
  
  ---
  
  