12 citations · 17 across the 3 of their papers we have counts for
3 papers
NoisyMix: Boosting Model Robustness to Common Corruptions
N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu +3
For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus ro…
Noisy Feature Mixup
Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera +2
We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data augmentation that combines the best of interpolation based training and noise injection schemes…
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
Francisco Utrera, Evan Kravitz, N. Benjamin Erichson +2
Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. This process consists of taking a n…