Data augmentation used to artificially expand the size and diversity of a dataset by applying various transformations to the existing data. These transformations can include operations such as rotating, flipping, cropping, zooming, and adjusting the brightness or contrast of images, or adding noise to textual data, depending on the domain. The goal of data augmentation is to create variations of the input data that are plausible but distinct from the original, thereby helping the model generalize better and avoid overfitting.
Key Benefits:
Improved Generalization: Data augmentation exposes the model to a wider variety of data, helping it generalize better to unseen examples and improving its performance.
Reduced Overfitting: By providing a larger set of training examples, data augmentation reduces the likelihood that the model will memorize the training data, preventing overfitting.
Limited Data Solution: When the available dataset is small, augmentation can artificially increase the dataset size, improving the robustness of the model.
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