AugLoss: A Robust Augmentation-based Fine Tuning Methodology

no code implementations • 5 Jun 2022 • Kyle Otstot, Andrew Yang, John Kevin Cava, Lalitha Sankar

As a step towards addressing both problems simultaneously, we introduce AugLoss, a simple but effective methodology that achieves robustness against both train-time noisy labeling and test-time feature distribution shifts by unifying data augmentation and robust loss functions.

Data Augmentation