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Data augmentation improves the performance and generalization of machine learning models by artificially increasing the size and diversity of the training dataset. In image classification tasks, techniques such as rotation, flipping, cropping, scaling, and color adjustments create varied versions of existing images, helping the model learn to recognize objects under different conditions.
In text classification, methods like synonym replacement, random word insertion, and back-translation introduce linguistic variations without altering the original meaning. By exposing models to a wider range of examples, data augmentation reduces overfitting, allowing models to generalize better to unseen data. This leads to more robust and accurate predictions, especially when the original dataset is limited or imbalanced.
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In image classification, augmentation techniques like rotations, flips, cropping, brightness adjustments, and noise injection create a much more diverse set of the same data. This helps models recognise features across different conditions that don't exactly match the original images. For text classification, augmentation techniques like synonym replacement, back translation, word deletion, and sentence shuffling introduce variations while preserving meaning. These methods help models handle paraphrasing, typos, and language variations, making them more adaptable to real-world use of language that often is not perfect.