Understanding Normalization and Unnormalization in Deep Learning

In the context of deep learning, normalization refers to the process of rescaling the input data to have a mean of 0 and a standard deviation of 1. This is typically done to prevent the model from being sensitive to the scale of the input data, and to improve the generalization of the model to new data.

Unnormalizing is the process of undoing the normalization transformation that was applied to the input data. This is typically done after the forward pass of the model, so that the output of the model is in the same scale as the input.

For example, if we have a model that takes an image as input and outputs a feature map, we might apply normalization to the input image by subtracting the mean and dividing by the standard deviation. After the forward pass, we would then unnormalize the output feature map by adding the mean and multiplying by the standard deviation. This ensures that the output feature map has the same scale as the input image.

Unnormalizing is an important step in many deep learning pipelines, as it allows us to compare the output of the model to the input, and to visualize the features learned by the model. It also allows us to use the same input data for multiple models, without having to worry about the scale of the input affecting the results.