<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>

CC BY-SA 4.0
2021-08-04T16:54:04.323Z
Is [this](https://arxiv.org/pdf/1707.00683v3.pdf) the paper that talks about this conditional BN you're referring to?
nbro
CC BY-SA 4.0
2021-08-04T17:44:51.943Z
@nbro Yes. I think the same is found in the PyTorch option for normalization with affine = True option. But... Yeah... The same paper...
hanugm
<comparison><batch-normalization>
Is there any difference between conditional batch normalization and batch normalization except the usage of MLPs for predicting $\beta$ and $\gamma$?
hanugm
# 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**: @username_1 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

