Body: <p>For example, in <a href="https://arxiv.org/pdf/1612.00593.pdf" rel="nofollow noreferrer">PointNet</a>, you see the 1D convolutions with the following channels <code>64 -&gt; 128 -&gt; 1024</code>. Why not e.g. <code>64 -&gt; 1024 -&gt; 1024</code> or <code>1024 -&gt; 1024 -&gt; 1024</code>?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-17T21:07:59.613Z
Comments.Text: @nbro It's not just PointNet, you see this design choice in pretty much all architectures.
Comments.UserDisplayName: user3180
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-07-18T16:57:02.373Z
Comments.Text: The number of kernels is typically associated with the number of different features that you want to detect in the input, although this is just an interpretation. So, in your case, if you increase the number of kernels (or channels), you are probably assuming that, as you process the input, more features should be detected. It would be nice if you could also provide other examples (just provide a link to the paper) where the number of channels or kernels increases as we go deeper, just for reference.
Comments.UserDisplayName: nbro
Tags: <convolutional-neural-networks><convolution><filters><convolutional-layers><1d-convolution>
Title: Why does the number of channels in the PointNet increase as we go deeper?
UserDisplayName: user3180
ThreadText: # Why does the number of channels in the PointNet increase as we go deeper?
  
  (Asked by: username_0 on 2020-07-17)
  
  For example, in [PointNet](https://arxiv.org/pdf/1612.00593.pdf), you see the 1D convolutions with the following channels `64 -> 128 -> 1024`. Why not e.g. `64 -> 1024 -> 1024` or `1024 -> 1024 -> 1024`?
  
  **Tags:** convolutional-neural-networks, convolution, filters, convolutional-layers, 1d-convolution
  > **Comments:**
  > **username_0**: @nbro It's not just PointNet, you see this design choice in pretty much all architectures.  
  > **username_1**: The number of kernels is typically associated with the number of different features that you want to detect in the input, although this is just an interpretation. So, in your case, if you increase the number of kernels (or channels), you are probably assuming that, as you process the input, more features should be detected. It would be nice if you could also provide other examples (just provide a link to the paper) where the number of channels or kernels increases as we go deeper, just for reference.  
  
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  ## 0 answers
  
  