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Why Image Augmentation is Essential in Deep Learning In computer vision, image augmentation plays a critical role in improving the generalization of deep neural networks. By artificially expanding the diversity of the training dataset through transformations that preserve the label, image augmentation helps reduce overfitting and increases model robustness. Especially for convolutional neural networks (CNNs) and vision transformers (ViTs), which learn hierarchical and spatial features, input variability introduced by augmentation forces the model to learn more invariant and meaningful representations. This is analogous to improving the mutual information between relevant features and output predictions while discarding noise. Common Image Augmentation Techniques and Parameter Descriptions 1. RandomHorizontalFlip Purpose:  Introduces horizontal symmetry by flipping the image left-to-right with a certain probability. from torchvision.transforms import v2 as transforms transform ...