AugMix improves model robustness and uncertainty.
problem Improving model performance under distribution shifts.
method AugMix: a simple data processing technique.
result Significantly improves robustness and uncertainty measures.
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4 results for “AugMix”
Improved model robustness against corruptions using online adaptation.
problem Machine vision models' vulnerability to image corruptions like blurring or compression artefacts.
method Using corrupted images' statistics for unsupervised online adaptation to improve robustness.
result ResNet-50 achieves 62.2% mCE on ImageNet-C with adaptation, improving from 76.7% without.
AugLoss combines data augmentation and robust loss functions for robust DL models.
problem Robustness against noisy labels and feature distribution shifts.
method Unified data augmentation and robust loss functions.
result AugLoss achieves gains over previous methods in various real-world dataset corruptions.
Adversarial training improves robustness against common corruptions.
problem Improving robustness of models against common corruptions.
method Adversarial training with perturbation and learned perceptual similarity.
result Our approach leads to state-of-the-art performance on common corruptions.