Body: <p>In her book Artificial Intelligence - A Guide for Thinking Humans, Melanie Mitchell explains that convolutional neural networks fail to build a useful model for these two classes of images, yielding chance classification for unseen images.</p>
  <p><a href="https://i.sstatic.net/40iCz.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/40iCz.png" alt="enter image description here" /></a></p>
  <p>Image from: <a href="https://www.foundalis.com/res/bps/bongard/p057.htm" rel="nofollow noreferrer">https://www.foundalis.com/res/bps/bongard/p057.htm</a></p>
  <p>This surprises me because convolutional networks look very close to the idea embedded in the images: they can detect an object regardless of its position. If splitted in two convenient regions, and taking any of them as a filter, the other region would be perfectly detected in the sameness case, and would yield a low match in the different class.</p>
  <p>How should this additional step of splitting be added to the convnet classifier to be able to model sameness?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-31T14:19:01.750Z
Comments.Text: Thanks. Do you mean attention as in transformers?
Comments.UserDisplayName: Jaume Oliver Lafont
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-03-31T21:40:32.230Z
Comments.Text: Without the splitting, I think that convolving the negative image with itself, then normalizing the maximum wrt the energy of the image, yields about 1/2 for sameness and close to 0 for different.
Comments.UserDisplayName: Jaume Oliver Lafont
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-04-01T10:49:47.707Z
Comments.Text: Yes. So in this scheme (ACNN, autoconvolutional neural networks), one single parameter is to be learned, the threshold to separate classes. This would be translation invariant, but not wrt rotation or size change.
Comments.UserDisplayName: Jaume Oliver Lafont
Tags: <convolutional-neural-networks>
Title: What else beyond convnets is needed to detect sameness in a Bongard problem?
UserDisplayName: Jaume Oliver Lafont
ThreadText: # What else beyond convnets is needed to detect sameness in a Bongard problem?
  
  (Asked by: username_0 on 2023-03-29)
  
  In her book Artificial Intelligence - A Guide for Thinking Humans, Melanie Mitchell explains that convolutional neural networks fail to build a useful model for these two classes of images, yielding chance classification for unseen images.
  
  [![enter image description here](https://i.sstatic.net/40iCz.png)](https://i.sstatic.net/40iCz.png)
  
  Image from: <https://www.foundalis.com/res/bps/bongard/p057.htm>
  
  This surprises me because convolutional networks look very close to the idea embedded in the images: they can detect an object regardless of its position. If splitted in two convenient regions, and taking any of them as a filter, the other region would be perfectly detected in the sameness case, and would yield a low match in the different class.
  
  How should this additional step of splitting be added to the convnet classifier to be able to model sameness?
  
  **Tags:** convolutional-neural-networks
  > **Comments:**
  > **username_0**: Thanks. Do you mean attention as in transformers?  
  > **username_0**: Without the splitting, I think that convolving the negative image with itself, then normalizing the maximum wrt the energy of the image, yields about 1/2 for sameness and close to 0 for different.  
  > **username_0**: Yes. So in this scheme (ACNN, autoconvolutional neural networks), one single parameter is to be learned, the threshold to separate classes. This would be translation invariant, but not wrt rotation or size change.  
  
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  ## 0 answers
  
  