ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
This work introduces ActiveAugment, a unified framework that treats augmentation selection as an online active learning problem, and reveals that the augmentation selection policy evolves meaningfully during training and that strategy choice has a direct impact on generalisation.
Noah Videcrantz, M. M. Ghazi
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