Group Normalization

Group normalization (GN) is a technique in deep learning designed to address limitations of batch normalization (BN), particularly its sensitivity to small batch sizes and performance inconsistencies in decentralized training settings like federated learning. Current research focuses on optimizing GN's performance across various architectures, including ResNets and U-Nets, investigating the optimal number of groups for improved gradient propagation, and comparing its efficacy against other normalization methods like layer normalization and kernel normalization in diverse applications. The impact of GN extends to improving model accuracy and stability in tasks such as image classification, segmentation (including biomedical image analysis), and privacy-preserving machine learning, offering a valuable alternative to BN in scenarios where its limitations are significant.