neural network robustness

Certifying Geometric Robustness of Neural Networks NeurIPS 2019 Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks NeurIPS 2019 Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks ICCV 2019 Improving Transferability of Adversarial Examples With Input Diversity CVPR 2019 Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification EMNLP 2019 Lower Bounds on Adversarial Robustness from Optimal Transport NeurIPS 2019 Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once ICCV 2019 On Robustness to Adversarial Examples and Polynomial Optimization NeurIPS 2019 On the Convergence and Robustness of Adversarial Training ICML 2019 On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions CVPR 2019 Provable Robustness of ReLU networks via Maximization of Linear Regions AISTATS 2019 PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach ICML 2019 Robustness of 3D Deep Learning in an Adversarial Setting CVPR 2019 Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem CVPR 2019 Fast and Effective Robustness Certification NeurIPS 2018 Improved Network Robustness with Adversary Critic NeurIPS 2018 Thermometer Encoding: One Hot Way To Resist Adversarial Examples ICLR 2018 With Friends Like These, Who Needs Adversaries? NeurIPS 2018 Lower bounds on the robustness to adversarial perturbations NeurIPS 2017 DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks CVPR 2016 Improving the Robustness of Deep Neural Networks via Stability Training CVPR 2016 Measuring Neural Net Robustness with Constraints NeurIPS 2016 Deeply Learned Face Representations Are Sparse, Selective, and Robust CVPR 2015