2 citations · 3 across the 3 of their papers we have counts for
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Using the Overlapping Score to Improve Corruption Benchmarks
Alfred Laugros, Alice Caplier, Matthieu Ospici
Neural Networks are sensitive to various corruptions that usually occur in real-world applications such as blurs, noises, low-lighting conditions, etc. To estimate the robustness o…
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training
Alfred Laugros, Alice Caplier, Matthieu Ospici
Despite their performance, Artificial Neural Networks are not reliable enough for most of industrial applications. They are sensitive to noises, rotations, blurs and adversarial ex…
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?
Alfred Laugros, Alice Caplier, Matthieu Ospici
Neural Networks have been shown to be sensitive to common perturbations such as blur, Gaussian noise, rotations, etc. They are also vulnerable to some artificial malicious corrupti…