Normalization Method

Normalization methods, crucial for preprocessing data in machine learning, aim to standardize feature scales and distributions to improve model performance and training stability. Current research focuses on developing adaptive normalization techniques tailored to specific data characteristics, such as non-stationary time series or heterogeneous datasets, often incorporating them into neural network architectures like Transformers and CNNs. These advancements are impacting diverse fields, from improving the accuracy of medical image analysis and financial forecasting to enhancing the efficiency of speech recognition systems and mitigating biases in face recognition.