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Does batch normalization replace dropout?
Because of this, and its regularizing effect, batch normalization has largely replaced dropout in modern convolutional architectures. As to why dropout is falling out of favor in recent applications, there are two main reasons. First, dropout is generally less effective at regularizing convolutional layers.
Should dropout be before or after activation?
Typically, dropout is applied after the non-linear activation function (a). However, when using rectified linear units (ReLUs), it might make sense to apply dropout before the non-linear activation (b) for reasons of computational efficiency depending on the particular code implementation.
Can you use dropout and batch normalization in convolution layers?
Both Dropout and Batch Normalization can be used with convolutional layers; but it recommended to use BN and not Dropout (see links below). Several tutorials apply BatchNormalization between Conv2D and Activation, before the MaxPooling2D
Why does dropout have to be after normalization?
Dropout is meant to block information from certain neurons completely to make sure the neurons do not co-adapt. So, the batch normalization has to be after dropout otherwise you are passing information through normalization statistics.
Can you use dropout and batch normalization in machine learning?
If you try to replicate that paper note very carefully the paragraph on the exceptions in placement for the beginning and end of the residual stacks. Both Dropout and Batch Normalization can be used with convolutional layers; but it recommended to use BN and not Dropout (see links below).
In order to maintain the representative power of the hidden neural network, batch normalization introduces two extra parameters — Gamma and Beta. Once we normalize the activation, we need to perform one more step to get the final activation value that can be feed as the input to another layer.