Group Normalization
Group Normalization (GN) is a normalization technique used in deep learning, particularly for convolutional neural networks (CNNs), to stabilize and accelerate training. It divides the channels of a feature map into groups and normalizes the activations within each group independently, reducing internal covariate shift without relying on batch statistics. This makes it effective for tasks with small batch sizes or batch-dependent constraints, such as object detection and video processing.
Developers should learn Group Normalization when working with CNNs in scenarios where batch normalization (BN) is impractical, such as with small batch sizes (e.g., in high-resolution images or memory-intensive models), or in tasks like object detection where batch statistics can vary significantly across samples. It is also useful for improving model robustness and performance in distributed training or when batch dependencies are undesirable, as it normalizes based on group-level statistics rather than batch-wide data.