Researchers have developed a new Padding Module for deep neural networks that can automatically learn to add pixels to the edges of images or feature maps. This Padding Module improves classification accuracy compared to traditional padding methods like zero padding. The module creates realistic extensions of input data, helping the neural network perform better on tasks. The experiments showed that the Padding Module outperformed other state-of-the-art competitors, such as VGG16 and ResNet50, by increasing classification accuracy by 1.23% and 0.44%, respectively.