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Found 3 papers, 2 papers with code
NoisyMix: Boosting Model Robustness to Common Corruptions
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 robustness of neural networks is an increasingly important topic.
Noisy Feature Mixup
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
Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains.
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