Image Data Augmentation
Image data augmentation enhances the performance of deep learning models in computer vision by artificially increasing the size and diversity of training datasets. Current research focuses on leveraging generative models, such as GANs and diffusion models, to create realistic synthetic images, as well as developing novel augmentation techniques that preserve crucial image information while introducing variability. These advancements improve model robustness, accuracy, and generalization capabilities across various applications, including object detection, image classification, and medical image analysis. The resulting improvements in model performance have significant implications for numerous fields relying on image-based analysis.
Papers
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