Body: <p>After a series of convolutions, I am up-sampling a compressed representation, I was curious what is the methodology I should follow to choose an optimum kernel size for up-sampling.</p>
  <ol>
  <li><p>How will the filter (or kernel) size affect the transpose convolution operation (e.g. when using <a href="https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html" rel="nofollow noreferrer"><code>ConvTranspose2d</code></a>)? Will a larger kernel help upsample with better detail or a small-sized kernel? And how would padding fit in this scenario?</p>
  </li>
  <li><p>At what rate should the depth(Channels i.e number of filters) decrease while upsampling i.e from (Dx24x24) to (D/2 or D/4, 48, 48)
  Eg: If i/p to TransposeConvolution is (cxhxw)64x8x8 how o/p quality be different for o/p of shape 32x16x16 and 16x16x16?</p>
  </li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-06-30T19:19:01.623Z
Comments.Text: Would you specify the purpose of up sampling and what final expected output of your convolution, thanks.
Comments.UserDisplayName: Cloud Cho
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-02T13:30:44.477Z
Comments.Text: @CloudCho I was looking for more of a general approach, so that while applying transpose convolution I could chose these hyperparameters more decisively.
Comments.UserDisplayName: Vortex
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-02T18:24:09.717Z
Comments.Text: thanks for details. How about Depth Decrease? Would you add more details in the question?
Comments.UserDisplayName: Cloud Cho
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-03T09:49:07.540Z
Comments.Text: @CloudCho I have edited the question.
Comments.UserDisplayName: Vortex
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-03T21:34:06.090Z
Comments.Text: Take a look at an [article](https://distill.pub/2016/deconv-checkerboard/) on a similar topic.
Comments.UserDisplayName: Aray Karjauv
Tags: <convolutional-neural-networks><pytorch><hyper-parameters><filters><transpose-convolution>
Title: How will the filter size affect the transpose convolution operation?
UserDisplayName: Vortex
ThreadText: # How will the filter size affect the transpose convolution operation?
  
  (Asked by: username_0 on 2020-12-26)
  
  After a series of convolutions, I am up-sampling a compressed representation, I was curious what is the methodology I should follow to choose an optimum kernel size for up-sampling.
  
  1. How will the filter (or kernel) size affect the transpose convolution operation (e.g. when using [`ConvTranspose2d`](https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html))? Will a larger kernel help upsample with better detail or a small-sized kernel? And how would padding fit in this scenario?
  2. At what rate should the depth(Channels i.e number of filters) decrease while upsampling i.e from (Dx24x24) to (D/2 or D/4, 48, 48)
     Eg: If i/p to TransposeConvolution is (cxhxw)64x8x8 how o/p quality be different for o/p of shape 32x16x16 and 16x16x16?
  
  **Tags:** convolutional-neural-networks, pytorch, hyper-parameters, filters, transpose-convolution
  > **Comments:**
  > **username_1**: Would you specify the purpose of up sampling and what final expected output of your convolution, thanks.  
  > **username_0**: @CloudCho I was looking for more of a general approach, so that while applying transpose convolution I could chose these hyperparameters more decisively.  
  > **username_1**: thanks for details. How about Depth Decrease? Would you add more details in the question?  
  > **username_0**: @CloudCho I have edited the question.  
  > **username_2**: Take a look at an [article](https://distill.pub/2016/deconv-checkerboard/) on a similar topic.  
  
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  ## 0 answers
  
  