adversarial robustness

ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies NeurIPS 2019 Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness NeurIPS 2019 Robustness May Be at Odds with Accuracy ICLR 2019 Robustness of 3D Deep Learning in an Adversarial Setting CVPR 2019 Robustness Verification of Classification Deep Neural Networks via Linear Programming CVPR 2019 Robustness via Curvature Regularization, and Vice Versa CVPR 2019 Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space IJCAI 2019 Scalable Verified Training for Provably Robust Image Classification ICCV 2019 Sensitivity Analysis of Deep Neural Networks AAAI 2019 ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness CVPR 2019 Sorting Out Lipschitz Function Approximation ICML 2019 Sparse and Imperceivable Adversarial Attacks ICCV 2019 SparseFool: A Few Pixels Make a Big Difference CVPR 2019 Theoretical Analysis of Adversarial Learning: A Minimax Approach NeurIPS 2019 Theoretical evidence for adversarial robustness through randomization NeurIPS 2019 Theoretically Principled Trade-off between Robustness and Accuracy ICML 2019 Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers NeurIPS 2019 Towards Adversarially Robust Object Detection ICCV 2019 Towards Better Accuracy and Robustness with Localized Adversarial Training AAAI 2019 Towards Robust ResNet: A Small Step but a Giant Leap IJCAI 2019 Towards the first adversarially robust neural network model on MNIST ICLR 2019 Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability ICLR 2019 Unlabeled Data Improves Adversarial Robustness NeurIPS 2019 Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty NeurIPS 2019 VC Classes are Adversarially Robustly Learnable, but Only Improperly COLT 2019