BatchNormalization

BatchNormalization is a technique used in neural network training to improve model stability and accelerate convergence. Introduced by Sergey Ioffe and Christian Szegedy in 2015, it normalizes the inputs of each layer by adjusting and scaling the activations, addressing issues related to internal covariate shift.

The process involves standardizing the inputs to each mini-batch during training, subtracting the batch mean and dividing by the batch standard deviation. This normalization ensures that the distribution of layer inputs remains consistent throughout training, which can lead to faster learning and improved performance. Additionally, BatchNormalization introduces learnable parameters, gamma (scale) and beta (shift), allowing the model to restore the original activations if necessary.

BatchNormalization is typically applied after the linear transformation and before the activation function within a layer. During training, it computes mean and variance estimates from the current mini-batch, while during inference, it uses moving averages of these statistics accumulated during training.

The benefits of BatchNormalization include reduced sensitivity to weight initialization, allowing for higher learning rates, and improved model generalization. It has become a standard component in deep neural network architectures, especially convolutional neural networks, due to its effectiveness in training very deep models.

However, BatchNormalization also has limitations, such as decreased effectiveness with very small batch sizes, where batch statistics may be noisy. Variants like Layer Normalization and Instance Normalization have been proposed to address these issues in specific contexts.

Overall, BatchNormalization is a fundamental technique that has significantly contributed to the advancement of deep learning methodologies.