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Found 2 papers, 1 papers with code
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions
In the field of adversarial robustness of image classifiers, robustness is commonly defined as the stability of a model to all input changes within a p-norm distance.
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
Furthermore, we observe unexpected optima in classifiers robust accuracy through training and testing classifiers with different levels of noise.
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