Batch Normalization is a widely used technique in deep learning that helps neural networks train faster and more reliably by normalizing the inputs to each layer. The idea behind batch normalization is to standardize the intermediate outputs (activations) of a neural network layer so that they have a mean of zero and a standard deviation of one, within each mini-batch during training. This process reduces the problem of “internal covariate shift,” which refers to the change in the distribution of layer inputs as the parameters of the previous layers are updated.
In practice, batch normalization is typically inserted between the linear transformation (weighted sum) and the activation function in a neural network layer. During training, it computes the mean and variance of the activations in the current mini-batch, then normalizes those activations. Two additional trainable parameters, gamma (scaling) and beta (shifting), allow the network to learn the optimal scale and shift for the normalized values, offering flexibility and preserving the layer’s representational power.
Batch normalization brings several benefits. First, it allows for higher learning rates, which speeds up the training process. Second, it acts as a form of regularization, helping reduce overfitting in some cases, sometimes even making other forms of regularization like dropout less necessary. Third, it can help neural networks become less sensitive to initialization, making training more stable and robust. These properties have made batch normalization almost a default component in many modern deep learning architectures, particularly for convolutional neural networks (CNNs) and deep feed-forward networks.
However, batch normalization does have some limitations. It relies on the statistics of each mini-batch, so its effectiveness can decrease with very small batch sizes. There can also be complications when applying it to recurrent neural networks (RNNs) or when using it during inference, as the statistics used for normalization need to be consistent between training and testing. To address this, moving averages of the batch statistics are often maintained during training for use at inference time.
Since its introduction, batch normalization has inspired the development of other normalization techniques, such as layer normalization and group normalization, which address some of its limitations in different scenarios. Despite these developments, batch normalization remains a key technique that has contributed significantly to the progress and practicality of deep learning.
