Data Augmentation in Computer Vision: Techniques and Applications
Abstract
Data augmentation is a key technique for improving the performance of machine learning models, particularly in computer vision. This paper surveys data augmentation methods such as geometric transformations, color adjustments, and adversarial augmentations. We evaluate their effectiveness on standard benchmarks and discuss how data augmentation can be extended to domains like video and 3D modeling.
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