Batch Normalization
Batch normalization is a technique used in training artificial neural networks to address the issue of internal covariate shift, where the distribution of inputs to layers deep within the network changes as the weights of the preceding layers are updated during training. This can slow down the training process and make it harder to use higher learning rates without risking divergence.
Batch normalization addresses this by normalizing the inputs to each layer so that they have a mean of zero and a unit variance. This is typically done by calculating the mean and variance of the inputs across the mini-batch and then scaling and shifting them to achieve the desired normalization.
Batch normalization not only stabilizes the learning process but also allows for the use of higher learning rates, potentially accelerating the convergence of the training process. Additionally, it can provide a slight regularization effect, reducing the need for other regularization techniques like dropout.
In deep learning, particularly in deep convolutional neural networks (CNNs) used for image recognition and classification tasks, batch normalization is applied after convolutional layers but before non-linear activation functions like ReLU. For instance, in a CNN designed to classify images into various categories, batch normalization layers inserted between the convolutional layers and their activation functions can help ensure that the scale of activations does not shift dramatically during training, leading to faster convergence and improved overall performance of the network.
Another application is in training Generative Adversarial Networks (GANs), where batch normalization can help stabilize the training process. GANs consist of two networks, a generator and a discriminator, which are trained simultaneously in a zero-sum game. Batch normalization applied within both networks helps to maintain stable gradients, making it easier to train GANs, which are often sensitive to the choice of hyperparameters and can be difficult to train without such stabilization techniques.