ZNorm: Z-Score Gradient Normalization for Deep Neural Networks

The rapid advancements in deep learning necessitate better training methods for deep neural networks (DNNs). As models grow in complexity, vanishing and exploding gradients impede performance. We propose Z-Score Normalization for Gradient Descent (ZNorm), an innovative technique that adjusts only the gradients to accelerate training and improve model performance. ZNorm normalizes the overall gradients, providing consistent gradient scaling across layers, thereby reducing the risks of vanishing and exploding gradients, having better performances. Our extensive experiments on CIFAR-10 and medical datasets demonstrate that ZNorm enhances performance metrics. ZNorm consistently outperforms existing methods, achieving superior results using the same experimental settings. In medical imaging applications, ZNorm improves tumor prediction and segmentation performances, underscoring its practical utility. These findings highlight ZNorm's potential as a robust and versatile tool for enhancing the training speed and effectiveness of deep neural networks across a wide range of architectures and applications.