Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

4 July 2018

Papers citing "Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations"

36 / 36 papers shown

Title
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 114 1,468 0 11 Dec 2018
Open Category Detection with PAC Guarantees Si Liu Risheek Garrepalli Thomas G. Dietterich Alan Fern Dan Hendrycks 35 84 0 01 Aug 2018
Do CIFAR-10 Classifiers Generalize to CIFAR-10? Benjamin Recht Rebecca Roelofs Ludwig Schmidt Vaishaal Shankar OOD FedML ELM 113 409 0 01 Jun 2018
Why do deep convolutional networks generalize so poorly to small image transformations? Aharon Azulay Yair Weiss 57 559 0 30 May 2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise Dan Hendrycks Mantas Mazeika Duncan Wilson Kevin Gimpel NoLa 122 553 0 14 Feb 2018
CondenseNet: An Efficient DenseNet using Learned Group Convolutions Gao Huang Shichen Liu Laurens van der Maaten Kilian Q. Weinberger 76 796 0 25 Nov 2017
Standard detectors aren't (currently) fooled by physical adversarial stop signs Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 37 59 0 09 Oct 2017
Provably Minimally-Distorted Adversarial Examples Nicholas Carlini Guy Katz Clark W. Barrett D. Dill AAML 56 89 0 29 Sep 2017
Towards Proving the Adversarial Robustness of Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel J. Kochenderfer AAML OOD 39 118 0 08 Sep 2017
Robust Physical-World Attacks on Deep Learning Models Kevin Eykholt Ivan Evtimov Earlence Fernandes Yue Liu Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno D. Song AAML 45 594 0 27 Jul 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 52 283 0 13 Jul 2017
Comparing deep neural networks against humans: object recognition when the signal gets weaker Robert Geirhos David H. J. Janssen Heiko H. Schutt Jonas Rauber Matthias Bethge Felix Wichmann 62 244 0 21 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 231 11,962 0 19 Jun 2017
Certified Defenses for Data Poisoning Attacks Jacob Steinhardt Pang Wei Koh Percy Liang AAML 73 751 0 09 Jun 2017
Shake-Shake regularization Xavier Gastaldi 3DPC BDL OOD 60 380 0 21 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 103 1,851 0 20 May 2017
A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions Samuel F. Dodge Lina Karam 3DH 48 421 0 06 May 2017
Google's Cloud Vision API Is Not Robust To Noise Hossein Hosseini Baicen Xiao Radha Poovendran AAML 52 123 0 16 Apr 2017
Quality Resilient Deep Neural Networks Samuel F. Dodge Lina Karam OOD 30 46 0 23 Mar 2017
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization Xun Huang Serge J. Belongie OOD 152 4,331 0 20 Mar 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 49 947 0 14 Feb 2017
Examining the Impact of Blur on Recognition by Convolutional Networks Igor Vasiljevic Ayan Chakrabarti Gregory Shakhnarovich 51 197 0 17 Nov 2016
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Zhuowen Tu Kaiming He 435 10,281 0 16 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 450 3,124 0 04 Nov 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 103 3,420 0 07 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 631 36,599 0 25 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 170 8,513 0 16 Aug 2016
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 231 8,030 0 13 Aug 2016
Early Methods for Detecting Adversarial Images Dan Hendrycks Kevin Gimpel AAML 56 236 0 01 Aug 2016
Defensive Distillation is Not Robust to Adversarial Examples Nicholas Carlini D. Wagner 35 339 0 14 Jul 2016
Measuring Neural Net Robustness with Constraints Osbert Bastani Yani Andrew Ioannou Leonidas Lampropoulos Dimitrios Vytiniotis A. Nori A. Criminisi AAML 50 423 0 24 May 2016
Improving the Robustness of Deep Neural Networks via Stability Training Stephan Zheng Yang Song Thomas Leung Ian Goodfellow OOD 30 637 0 15 Apr 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.4K 192,638 0 10 Dec 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 48 3,061 0 14 Nov 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 328 43,154 0 11 Feb 2015
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 185 14,831 1 21 Dec 2013