Data Augmentation (Beyond Basic Transformations)
Advanced data augmentation goes beyond simple flips or rotations to generate new, realistic training samples, increasing dataset diversity and model robustness. Techniques include Mixup, which creates virtual samples by linearly interpolating between existing data points and their labels, or CutMix, which patches regions from one image onto another, boosting generalization.
It's like taking two different recipe ingredients and blending them to create a new, plausible ingredient that helps a chef learn to distinguish flavors even better.
It significantly reduces overfitting, improves model generalization, and is vital when real-world data collection is scarce or expensive.
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