Answers.Body: <p>Well, I think, this statement sounds somehow misleading.</p>
  <p>The main statement is actually in the first passage of this statement:</p>
  <blockquote>
  <p>Note that the use of convolution for processing variably sized inputs
  makes sense only for inputs that have variable size because they
  contain varying amounts of <strong>observation of the same kind</strong> of
  thing—different lengths of recordings over time, diﬀerent widths of
  observations over space, and so forth.</p>
  </blockquote>
  <p>Convolution is applicable only provided the input is the 1D, 2D, 3D array, graph, any collection of the data of the same kind. Each pixel is the feature vector of the same shape.</p>
  <p>This statement means, that one cannot apply convolutions if the inputs are not-structured, like the data tables, which involve <em>boolean, categorical, continuous</em> features. There is no notion of locality and adjacency for this kind of data.</p>
  
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Answers.CreationDate: 2021-10-26T09:06:08.540Z
Answers.LastActivityDate: 2021-10-26T11:28:51.807Z
Answers.LastEditDate: 2021-10-26T11:28:51.807Z
Answers.UserDisplayName: spiridon_the_sun_rotator
Body: <p>Convolutional neural networks are capable of handling inputs of varying sizes. It is one of the key benefits of convolutional neural networks. But I am unsure about the cases when we should <em>not</em> utilize this advantage of the convolutional neural network.</p>
  <p>Although the following example has been provided in the chapter named <a href="https://www.deeplearningbook.org/contents/convnets.html#pf1e" rel="nofollow noreferrer">Convolutional Networks</a> of the textbook titled <a href="https://www.deeplearningbook.org/" rel="nofollow noreferrer">Deep Learning</a> by <em>Ian Goodfellow et al</em>.</p>
  <blockquote>
  <p>Convolution does not make sense if the input has variable size because
  it can optionally include diﬀerent kinds of observations. <strong>For example,
  if we are processing college applications, and our features consist of
  both grades and standardized test scores, but not every applicant took
  the standardized test, then it does not make sense to convolve the
  same weights over features corresponding to the grades as well as the
  features corresponding to the test scores.</strong></p>
  </blockquote>
  <p>It is not clear for me to understand the above example since I am habituated with images in the case of convolutional neural networks.</p>
  <p>Can anyone provide a possible example of images where we cannot utilize the benefit of passing variable-size images to the convolutional neural network?</p>
  
Tags: <convolutional-neural-networks><image-processing>
Title: Simple Image-based example for not utilising the variable-sized input handling capability of a Convolutional neural network
UserDisplayName: hanugm
ThreadText: # Simple Image-based example for not utilising the variable-sized input handling capability of a Convolutional neural network
  
  (Asked by: username_0 on 2021-10-25)
  
  Convolutional neural networks are capable of handling inputs of varying sizes. It is one of the key benefits of convolutional neural networks. But I am unsure about the cases when we should *not* utilize this advantage of the convolutional neural network.
  
  Although the following example has been provided in the chapter named [Convolutional Networks](https://www.deeplearningbook.org/contents/convnets.html#pf1e) of the textbook titled [Deep Learning](https://www.deeplearningbook.org/) by *Ian Goodfellow et al*.
  
  > Convolution does not make sense if the input has variable size because
  > it can optionally include diﬀerent kinds of observations. **For example,
  > if we are processing college applications, and our features consist of
  > both grades and standardized test scores, but not every applicant took
  > the standardized test, then it does not make sense to convolve the
  > same weights over features corresponding to the grades as well as the
  > features corresponding to the test scores.**
  
  It is not clear for me to understand the above example since I am habituated with images in the case of convolutional neural networks.
  
  Can anyone provide a possible example of images where we cannot utilize the benefit of passing variable-size images to the convolutional neural network?
  
  **Tags:** convolutional-neural-networks, image-processing
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-10-26. Score: 1
  
  Well, I think, this statement sounds somehow misleading.
  
  The main statement is actually in the first passage of this statement:
  
  > Note that the use of convolution for processing variably sized inputs
  > makes sense only for inputs that have variable size because they
  > contain varying amounts of **observation of the same kind** of
  > thing—different lengths of recordings over time, diﬀerent widths of
  > observations over space, and so forth.
  
  Convolution is applicable only provided the input is the 1D, 2D, 3D array, graph, any collection of the data of the same kind. Each pixel is the feature vector of the same shape.
  
  This statement means, that one cannot apply convolutions if the inputs are not-structured, like the data tables, which involve *boolean, categorical, continuous* features. There is no notion of locality and adjacency for this kind of data.
  
  
  ---
  
  