What is batch normalization in deep learning?

Batch normalization is a technique used to improve the training speed and stability of neural networks. It works by normalizing the inputs to each layer in a way that adjusts their mean and variance during training. This helps mitigate issues like internal covariate shift, where changes in the distribution of layer inputs slow down training by forcing later layers to constantly adapt. By standardizing these inputs, batch normalization allows the network to learn more efficiently with higher learning rates and reduces sensitivity to initial weight values.

The process involves two main steps during training. First, for each mini-batch of data, the layer’s outputs are normalized by subtracting the batch mean and dividing by the batch standard deviation. This centers the data around zero and scales it to unit variance. Second, the normalized values are scaled and shifted using two learnable parameters, gamma (γ) and beta (β), which restore the network’s ability to represent nonlinear transformations. For example, in a convolutional neural network (CNN), batch normalization is typically applied after a convolution layer but before a non-linear activation like ReLU. During inference, instead of using batch-specific statistics, the model uses running averages of mean and variance computed during training to maintain consistency.

Key benefits include faster convergence and reduced dependency on careful weight initialization. For instance, training a deep ResNet without batch normalization might require meticulous tuning of learning rates, whereas with batch normalization, the network converges reliably even with simpler initializations. Additionally, the noise introduced by mini-batch statistics acts as a mild regularizer, reducing overfitting in some cases. However, batch normalization adds computational overhead and can behave unexpectedly with very small batch sizes. Developers often use it in architectures where deep layers or saturating activation functions (like sigmoid) are present, as it helps maintain stable gradients. In practice, frameworks like TensorFlow or PyTorch implement it through layers (e.g., BatchNorm2d), handling the statistics tracking automatically.