Augmentation

Augmentation refers to the process of enhancing or improving the performance of a machine learning model by adding additional data or features to the existing dataset. This can be done by either adding more data to the training set or by generating synthetic data using techniques such as data augmentation. Augmentation can also refer to the use of external data sources to supplement the existing dataset, such as using pre-trained models or incorporating data from other domains. The goal of augmentation is to improve the generalization and robustness of the model, allowing it to perform better on new, unseen data.
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