Based Normalization

Based normalization techniques aim to improve the training and performance of neural networks by addressing issues like internal covariate shift and inconsistent data distributions. Current research explores various approaches, including cluster-based normalization which adapts to data heterogeneity, and patch-based normalization which enhances local coherence in image processing tasks. These advancements are impacting diverse fields, from improving the accuracy of remaining useful life predictions in industrial systems to enhancing the realism of image harmonization. The overall goal is to develop more robust and efficient normalization methods tailored to specific network architectures and data characteristics.