Nearest Neighbor Projection Removal Adversarial Training projects out inter-class dependencies in feature space during training, claims to reduce the Lipschitz constant and Rademacher complexity, and reports competitive robust accuracy on CIFAR-10, CIFAR-100, SVHN, and TinyImagenet.
The dimpled manifold model of adversarial examples in machine learning
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, parameters, epochs, and data than prior defenses.
citing papers explorer
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Nearest Neighbor Projection Removal Adversarial Training
Nearest Neighbor Projection Removal Adversarial Training projects out inter-class dependencies in feature space during training, claims to reduce the Lipschitz constant and Rademacher complexity, and reports competitive robust accuracy on CIFAR-10, CIFAR-100, SVHN, and TinyImagenet.
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A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, parameters, epochs, and data than prior defenses.