Kernel normalization improves performance and efficiency in deep learning architectures by addressing the limitations of BatchNorm.
Kernel Normalization (KernelNorm) and kernel normalized convolutional layers offer a solution to the limitations of BatchNorm, providing higher performance and efficiency in deep CNNs.