CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.
Provable defense against geometric transformations
2 Pith papers cite this work. Polarity classification is still indexing.
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Super-DeepG improves linear relaxation techniques and Lipschitz optimization for neural network robustness certification against geometric perturbations, with a GPU implementation that claims better precision and speed than prior work.
citing papers explorer
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Towards Generalized Certified Robustness with Multi-Norm Training
CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.
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Certified geometric robustness -- Super-DeepG
Super-DeepG improves linear relaxation techniques and Lipschitz optimization for neural network robustness certification against geometric perturbations, with a GPU implementation that claims better precision and speed than prior work.