Exploring Data Augmentation and Active Learning Benefits in Imbalanced Datasets
Luis Moles,
Alain Andres,
Goretti Echegaray
et al.
Abstract:Despite the increasing availability of vast amounts of data, the challenge of acquiring labeled data persists. This issue is particularly serious in supervised learning scenarios, where labeled data are essential for model training. In addition, the rapid growth in data required by cutting-edge technologies such as deep learning makes the task of labeling large datasets impractical. Active learning methods offer a powerful solution by iteratively selecting the most informative unlabeled instances, thereby redu… Show more
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