BatchNorm Layer

Batch normalization (BatchNorm) is a technique used in deep neural networks to stabilize training and improve performance by normalizing the activations of neurons. Current research focuses on addressing challenges posed by BatchNorm in diverse settings, such as federated learning and test-time adaptation, often involving modifications to the standard BatchNorm layer or exploring alternative normalization strategies like L1 or TopK BatchNorm. These efforts aim to enhance the efficiency and robustness of BatchNorm, particularly in resource-constrained environments and scenarios with non-independent and identically distributed (non-i.i.d.) data. Improved understanding of BatchNorm's impact on optimization trajectories and its interaction with other training components, like weight decay and momentum, is also a key area of investigation.