Understanding Measures of Uncertainty for Adversarial Example Detection

22 March 2018

Papers citing "Understanding Measures of Uncertainty for Adversarial Example Detection"

11 / 211 papers shown

Title
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network Xuanqing Liu Yao Li Chongruo Wu Cho-Jui Hsieh AAML OOD 19 171 0 01 Oct 2018
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning Daniel Coelho De Castro Jeremy Tan Bernhard Kainz E. Konukoglu Ben Glocker DRL 9 69 0 27 Sep 2018
Discriminative out-of-distribution detection for semantic segmentation Petra Bevandić Ivan Kreso Marin Orsic Sinisa Segvic 34 78 0 23 Aug 2018
Scalable Multi-Class Bayesian Support Vector Machines for Structured and Unstructured Data Martin Wistuba Ambrish Rawat BDL 14 2 0 07 Jun 2018
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks Y. Gal Lewis Smith AAML BDL 36 34 0 02 Jun 2018
VectorDefense: Vectorization as a Defense to Adversarial Examples V. Kabilan Brandon L. Morris Anh Totti Nguyen AAML 14 21 0 23 Apr 2018
Adversarial Training Versus Weight Decay A. Galloway T. Tanay Graham W. Taylor AAML 19 23 0 10 Apr 2018
Are Generative Classifiers More Robust to Adversarial Attacks? Yingzhen Li John Bradshaw Yash Sharma AAML 49 78 0 19 Feb 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 179 1,185 0 30 Nov 2014