Benjamin Erichson

NoisyMix: Boosting Model Robustness to Common Corruptions

The robustness of neural networks has become increasingly important in real-world applications where stable and reliable performance is valued over simply achieving high predictive accuracy. To address this, data augmentation techniques have been shown to improve robustness against input perturbatio…