ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, either relying on expensive dataset-specific policy search or applying transformations uniformly at random, regardless of what the model has already learned. We introduce ActiveAugment, a unified framework that treats augmentation selection as an online active learning problem. For each training minibatch, ActiveAugment generates a pool of candidate augmented views and scores each candidate using a combination of the model's predictive uncertainty and the feature discrepancy induced by the augmentation. The augmentation under which the current mod...
| Authors | Noah Videcrantz, Mostafa Mehdipour Ghazi |
|---|---|
| Primary category | cs.CV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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