<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>

CC BY-SA 4.0
2021-08-08T23:31:42.180Z
What is the output in this case? It is a single real number?
hanugm
CC BY-SA 4.0
2021-08-09T06:20:01.147Z
@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'
hridayns
<machine-learning><convolutional-neural-networks><image-processing><convolutional-layers><filters>
What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?
hridayns
# 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**: @username_1 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

