Batch Norm

Batch normalization (BatchNorm) is a technique used in deep neural networks to stabilize training and improve performance by normalizing the activations of each layer. Current research focuses on addressing BatchNorm's limitations in handling distribution shifts between training and testing data, particularly in scenarios with noisy or corrupted inputs, small batch sizes, and imbalanced data. This involves exploring alternative normalization methods (e.g., group or layer normalization) and developing techniques to improve the robustness and stability of BatchNorm during test-time adaptation, often incorporating methods like principal component analysis or adversarial example generation. These advancements are crucial for deploying robust and reliable deep learning models in real-world applications where data variability is common.