The difference between batch normalization and layer normalization

Normalization is often used to solve the problem of exploding or vanishing gradients in neural networks. It works by mapping the values ​​of a feature into the range [0,1] so that all values ​​are in the same scale or distribution. Simply put, normalization normalizes the input to a neural network and increases training speed.

Two types of normalization

There are mainly two types of normalization techniques, namely:

• Batch normalization
• Layer Normalization

Batch Normalization

In order to obtain the output of the hidden layer, we usually use a nonlinear activation function to process the input. And for each neuron in a specific layer, we can preactivate it so that it has zero mean and unit standard deviation. This can be achieved by performing mean subtraction and standard deviation division on a mini-batch of input features.

However, forcing all pre-activations to zero and unit standard deviation for all batches may be too strict, so introducing a certain fluctuation distribution can better help the network learn.

In order to solve this problem, batch normalization introduces two parameters: the scale factor gamma (γ) and the offset beta (β), both of which are learnable parameters.

In batch normalization, we need to pay attention to the use of batch statistics. When the batch size is small, the sample mean and standard deviation are not enough to represent the actual distribution, which results in the network failing to learn anything meaningful. Therefore, we need to ensure that the batch size is large enough to obtain more accurate statistics, thereby improving the performance and learning of the model.

Layer Normalization

Layer normalization is a method proposed by researchers Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. The core idea of ​​this method is to have the same distribution for all features of a given input over all neurons in a specific layer. Different from batch normalization, layer normalization performs a normalization operation on the feature dimension of each sample. It normalizes the output of this layer by calculating the mean and variance of each neuron on the input features. This method can help the model adapt to small batches of data and improve the generalization ability of the model. The advantage of layer normalization is that it does not rely on batch

to normalize all features, but normalizes each input to a specific layer, eliminating the dependence on batches. This makes layer normalization well suited for sequence models such as the popular Transformer and Recurrent Neural Networks (RNN).

Main differences between batch normalization and layer normalization

1. Batch normalization normalizes each feature independently in a mini-batch. Layer normalization normalizes each input in the batch independently across all features.

2. Since batch normalization depends on the batch size, it is not effective for small batches. Layer normalization is batch size independent, so it can be applied to batches of smaller sizes as well.

3. Batch normalization requires different processing during training and inference. Since layer normalization is done along the input length of a specific layer, the same set of operations can be used at training and inference time.

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