Batch normalization is a technique used in deep learning to stabilize and accelerate the training process of neural networks. It works by normalizing the inputs to each layer, ensuring that they have a mean of zero and a standard deviation of one. This is done for each mini-batch of data rather than the entire dataset, hence the term "batch" normalization. By doing this, batch normalization helps reduce internal covariate shift—the change in the distribution of network activations due to weight updates during training—making the network more robust and efficient.
When implementing batch normalization, a layer in the network calculates the mean and variance of its inputs for the current batch. It then uses these statistics to normalize the inputs. After normalization, the layer can scale and shift the output using learned parameters. This allows the network to retain the expressiveness it needs to learn complex functions while also benefiting from the regularizing effect of normalization. For example, if you are training a convolutional neural network (CNN) for image classification, incorporating batch normalization can make it converge faster, allowing it to reach a good accuracy with fewer epochs.
Batch normalization can also help mitigate issues like vanishing and exploding gradients, which are common in deep networks. By maintaining stable distributions for the inputs of each layer, it allows deeper networks to train more effectively. In practice, many developers find that adding batch normalization layers improves the performance of their models. It has become a standard practice in training modern architectures, including popular models like ResNet and Inception, showing that it can yield significant benefits across various tasks and datasets.