Body: <p>Batch normalization in neural networks uses <span class="math-container">$\beta$</span> and <span class="math-container">$\gamma$</span> for scaling. The analytical formula is given by</p>
  <p><span class="math-container">$$\dfrac{x - \mathbb{E}[x]}{\sqrt{Var(X)}}* \gamma + \beta$$</span></p>
  <p>Conditional batch normalization uses multi-layer perceptrons to calculate the values of <span class="math-container">$\gamma$</span> and <span class="math-container">$\beta$</span> instead of giving fixed values to them.</p>
  <p>Is it only the difference between them or is there any other fundamental difference between them in terms of functionality?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-04T16:54:04.323Z
Comments.Text: Is [this](https://arxiv.org/pdf/1707.00683v3.pdf) the paper that talks about this conditional BN you're referring to?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-04T17:44:51.943Z
Comments.Text: @nbro Yes. I think the same is found in the PyTorch option for normalization with affine = True option. But... Yeah... The same paper...
Comments.UserDisplayName: hanugm
Tags: <comparison><batch-normalization>
Title: Is there any difference between conditional batch normalization and batch normalization except the usage of MLPs for predicting $\beta$ and $\gamma$?
UserDisplayName: hanugm
ThreadText: # Is there any difference between conditional batch normalization and batch normalization except the usage of MLPs for predicting $\beta$ and $\gamma$?
  
  (Asked by: username_0 on 2021-08-03)
  
  Batch normalization in neural networks uses $\beta$ and $\gamma$ for scaling. The analytical formula is given by
  
  $$\dfrac{x - \mathbb{E}[x]}{\sqrt{Var(X)}}\* \gamma + \beta$$
  
  Conditional batch normalization uses multi-layer perceptrons to calculate the values of $\gamma$ and $\beta$ instead of giving fixed values to them.
  
  Is it only the difference between them or is there any other fundamental difference between them in terms of functionality?
  
  **Tags:** comparison, batch-normalization
  > **Comments:**
  > **username_1**: Is [this](https://arxiv.org/pdf/1707.00683v3.pdf) the paper that talks about this conditional BN you're referring to?  
  > **username_0**: @nbro Yes. I think the same is found in the PyTorch option for normalization with affine = True option. But... Yeah... The same paper...  
  
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  ## 0 answers
  
  