Rethinking Normalization

Normalization techniques, crucial for stabilizing and improving deep learning model performance, are undergoing significant reevaluation across diverse applications. Current research focuses on developing alternative normalization methods tailored to specific architectures and data types, such as adapting normalization for transformers in time series analysis or addressing its limitations in federated learning and image super-resolution. These efforts aim to overcome challenges like information loss, computational intractability, and performance degradation in various contexts, ultimately leading to more robust and efficient deep learning models.