Instance Normalization - Techniques and Applications: Examining instance normalization techniques and their applications in stabilizing and accelerating training in deep neural networks

Abstract

Instance normalization (IN) has emerged as a powerful tool in the realm of deep neural networks (DNNs), offering a means to stabilize and accelerate training. Unlike batch normalization, which computes normalization statistics over a batch of samples, IN normalizes each sample individually, making it suitable for style transfer, super-resolution, and other tasks where batch statistics might not be ideal. This paper provides an in-depth analysis of various IN techniques, including their theoretical foundations, implementation details, and comparative performance evaluations. Additionally, it explores the wide range of applications where IN has shown remarkable effectiveness, such as image generation, image-to-image translation, and domain adaptation. By shedding light on the nuances of IN, this paper aims to deepen the understanding of normalization techniques in DNNs and inspire further research in this exciting field.