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Testing DNN Image Classifiers for Confusion & Bias Errors

20 May 2019

Papers citing "Testing DNN Image Classifiers for Confusion & Bias Errors"

50 / 57 papers shown

Title
Metric Learning for Adversarial Robustness Chengzhi Mao Ziyuan Zhong Junfeng Yang Carl Vondrick Baishakhi Ray OOD 80 185 0 03 Sep 2019
Noise-tolerant fair classification A. Lamy Ziyuan Zhong A. Menon Nakul Verma NoLa 65 77 0 30 Jan 2019
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing Jingyi Wang Guoliang Dong Jun Sun Xinyu Wang Peixin Zhang AAML 48 191 0 14 Dec 2018
Feature Denoising for Improving Adversarial Robustness Cihang Xie Yuxin Wu Laurens van der Maaten Alan Yuille Kaiming He 125 912 0 09 Dec 2018
Guiding Deep Learning System Testing using Surprise Adequacy Jinhan Kim R. Feldt S. Yoo AAML ELM 74 432 0 25 Aug 2018
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing Augustus Odena Ian Goodfellow AAML 68 322 0 28 Jul 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 78 450 0 31 May 2018
Concolic Testing for Deep Neural Networks Youcheng Sun Min Wu Wenjie Ruan Xiaowei Huang Marta Kwiatkowska Daniel Kroening 80 334 0 30 Apr 2018
Formal Security Analysis of Neural Networks using Symbolic Intervals Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 84 478 0 28 Apr 2018
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems Lei Ma Felix Juefei Xu Fuyuan Zhang Jiyuan Sun Minhui Xue ... Ting Su Li Li Yang Liu Jianjun Zhao Yadong Wang ELM 67 624 0 20 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 151 508 0 13 Mar 2018
Fairness Through Computationally-Bounded Awareness Michael P. Kim Omer Reingold G. Rothblum FaML 87 146 0 08 Mar 2018
Empirical Risk Minimization under Fairness Constraints Michele Donini L. Oneto Shai Ben-David John Shawe-Taylor Massimiliano Pontil FaML 78 445 0 23 Feb 2018
Visual Interpretability for Deep Learning: a Survey Quanshi Zhang Song-Chun Zhu FaML HAI 144 822 0 02 Feb 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 117 969 0 29 Jan 2018
Adversarial Examples: Attacks and Defenses for Deep Learning Xiaoyong Yuan Pan He Qile Zhu Xiaolin Li SILM AAML 99 1,625 0 19 Dec 2017
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems Kexin Pei Linjie Zhu Yinzhi Cao Junfeng Yang Carl Vondrick Suman Jana AAML 64 103 0 05 Dec 2017
Fairness Testing: Testing Software for Discrimination Sainyam Galhotra Yuriy Brun A. Meliou 69 380 0 11 Sep 2017
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars Yuchi Tian Kexin Pei Suman Jana Baishakhi Ray AAML 70 1,360 0 28 Aug 2017
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Yinpeng Dong Hang Su Jun Zhu Fan Bao AAML 130 129 0 18 Aug 2017
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints Jieyu Zhao Tianlu Wang Mark Yatskar Vicente Ordonez Kai-Wei Chang FaML 111 974 0 29 Jul 2017
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 88 282 0 12 Jul 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 293 2,271 0 24 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini Basel Alomair AAML 96 242 0 15 Jun 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 109 1,371 0 18 May 2017
Extending Defensive Distillation Nicolas Papernot Patrick McDaniel AAML 67 119 0 15 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 158 1,526 1 19 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 97 1,273 0 04 Apr 2017
Counterfactual Fairness Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo M. A. Silva FaML 228 1,588 0 20 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 105 894 0 01 Mar 2017
Adversarial examples for generative models Jernej Kos Ian S. Fischer Basel Alomair GAN 86 274 0 22 Feb 2017
On the (Statistical) Detection of Adversarial Examples Kathrin Grosse Praveen Manoharan Nicolas Papernot Michael Backes Patrick McDaniel AAML 86 714 0 21 Feb 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 73 950 0 14 Feb 2017
Adversarial Attacks on Neural Network Policies Sandy Huang Nicolas Papernot Ian Goodfellow Yan Duan Pieter Abbeel MLAU AAML 102 839 0 08 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 321 1,875 0 03 Feb 2017
Simple Black-Box Adversarial Perturbations for Deep Networks Nina Narodytska S. Kasiviswanathan AAML 74 240 0 19 Dec 2016
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 208 1,214 0 26 Oct 2016
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 245 944 0 21 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 236 4,337 0 07 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 335 20,110 0 07 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 547 5,912 0 08 Jul 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,708 0 10 Jun 2016
Measuring Neural Net Robustness with Constraints Osbert Bastani Yani Andrew Ioannou Leonidas Lampropoulos Dimitrios Vytiniotis A. Nori A. Criminisi AAML 99 424 0 24 May 2016
Improving the Robustness of Deep Neural Networks via Stability Training Stephan Zheng Yang Song Thomas Leung Ian Goodfellow OOD 58 639 0 15 Apr 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,685 0 08 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,510 0 10 Dec 2015
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 117 3,968 0 24 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 118 3,077 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014