Data Augmentation

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Data augmentation is a technique used to increase the size and variety of a training dataset by creating modified versions of existing examples. The key idea is that these transformations keep the original label the same while giving the model more diverse inputs to learn from. In computer vision, this can include flipping or cropping images, adjusting colors, or blending images together. For text, it might mean replacing words with synonyms or masking certain tokens.

The purpose of augmentation is to help the model handle real-world variation and avoid overfitting. Teams choose transformations that make sense for the task so the model learns useful patterns instead of being confused by unrealistic changes. Improvements are typically measured on validation or test sets rather than just looking at training loss.

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