Normalization Techniques in Deep Learning: Examining various normalization techniques such as batch normalization, layer normalization, and group normalization in deep neural networks

Abstract

In recent years, deep learning has become a cornerstone of artificial intelligence, enabling remarkable progress across various domains. Normalization techniques play a pivotal role in training deep neural networks, ensuring stable convergence and improved generalization. This paper provides a comprehensive overview of normalization techniques in deep learning, focusing on batch normalization, layer normalization, and group normalization. We delve into the theoretical foundations, practical implementations, and comparative analyses of these techniques, highlighting their impact on model training, convergence speed, and generalization performance. Through extensive experimentation and evaluation, we demonstrate the strengths and limitations of each normalization technique, providing insights for selecting the most appropriate method based on the characteristics of the dataset and model architecture. Additionally, we discuss recent advancements and future research directions in normalization techniques, aiming to enhance the understanding and utilization of these methods in the deep learning community.