Instance Normalization

Instance normalization (IN) is a normalization technique used in deep learning to stabilize training and improve the robustness of neural networks, particularly in scenarios with significant variations in input data distribution. Current research focuses on extending IN's capabilities, including adaptive instance normalization (AdaIN) for style transfer and domain adaptation, and variations like frequency-adaptive normalization for time series forecasting. These advancements are improving performance in diverse applications such as image synthesis, style transfer, acoustic scene classification, and time series prediction, demonstrating IN's significant impact on various computer vision and signal processing tasks.