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[r/ML] [D] Image Augmentation in Practice: In-Distribution vs OOD Augmentations, TTA, and the Manifold View

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Summary

A seasoned computer vision practitioner has authored a comprehensive guide on image augmentation, drawing from a decade of experience and years with Albumentations. The guide categorizes augmentations into two regimes: 'in-distribution' for simulating realistic data variations and 'out-of-distribution' for applying intentionally unrealistic transformations as regularization. This practical framework aims to enhance model robustness and performance.

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