Image Augmentation

Image augmentation artificially expands training datasets by modifying existing images, improving the robustness and accuracy of machine learning models, particularly in scenarios with limited data. Current research focuses on optimizing augmentation strategies, including exploring the effectiveness of various transformations (geometric, color, and even physics-based simulations), and integrating augmentations within different model architectures like convolutional neural networks (CNNs), transformers, and diffusion models for tasks such as image classification, object detection, and semantic segmentation. This work is significant because it enhances the performance and generalizability of computer vision models across diverse applications, from medical diagnosis to autonomous driving, by mitigating issues like overfitting and distribution shifts.