Body: <p>I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application would decrease if someone used <code>kernel size = the size of the input image</code> itself?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-08T23:31:42.180Z
Comments.Text: What is the output in this case? It is a single real number?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-09T06:20:01.147Z
Comments.Text: @hanugm Sure, let's assume that it has a two cases - one with a single real-valued output and the other a probability output vector of size 'n'
Comments.UserDisplayName: hridayns
Tags: <machine-learning><convolutional-neural-networks><image-processing><convolutional-layers><filters>
Title: What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?
UserDisplayName: hridayns
ThreadText: # What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?
  
  (Asked by: username_0 on 2021-08-08)
  
  I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application would decrease if someone used `kernel size = the size of the input image` itself?
  
  **Tags:** machine-learning, convolutional-neural-networks, image-processing, convolutional-layers, filters
  > **Comments:**
  > **username_1**: What is the output in this case? It is a single real number?  
  > **username_0**: @hanugm Sure, let's assume that it has a two cases - one with a single real-valued output and the other a probability output vector of size 'n'  
  
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  ## 0 answers
  
  