Robustness May Be at Odds with Accuracy

30 May 2018

Papers citing "Robustness May Be at Odds with Accuracy"

50 / 433 papers shown

Title
Query complexity of adversarial attacks Grzegorz Gluch R. Urbanke AAML 27 5 0 02 Oct 2020
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients Yifei Huang Yaodong Yu Hongyang R. Zhang Yi Ma Yuan Yao AAML 37 26 0 28 Sep 2020
Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing Maurice Weber Nana Liu Bo-wen Li Ce Zhang Zhikuan Zhao AAML 37 28 0 21 Sep 2020
Defending Against Multiple and Unforeseen Adversarial Videos Shao-Yuan Lo Vishal M. Patel AAML 31 23 0 11 Sep 2020
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent Ricardo Bigolin Lanfredi Joyce D. Schroeder Tolga Tasdizen 27 11 0 10 Sep 2020
SoK: Certified Robustness for Deep Neural Networks Linyi Li Tao Xie Bo-wen Li AAML 33 128 0 09 Sep 2020
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks Wei-An Lin Chun Pong Lau Alexander Levine Ramalingam Chellappa S. Feizi AAML 81 60 0 05 Sep 2020
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings John Mitros A. Pakrashi Brian Mac Namee UQCV 26 2 0 03 Sep 2020
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training Alfred Laugros A. Caplier Matthieu Ospici AAML 24 19 0 19 Aug 2020
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 21 3 0 07 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 29 73 0 07 Aug 2020
Entropy Guided Adversarial Model for Weakly Supervised Object Localization Sabrina Narimene Benassou Wuzhen Shi Feng Jiang GAN AAML WSOL 25 5 0 04 Aug 2020
Robust Machine Learning via Privacy/Rate-Distortion Theory Ye Wang Shuchin Aeron Adnan Siraj Rakin T. Koike-Akino P. Moulin OOD 22 6 0 22 Jul 2020
Adversarial Training Reduces Information and Improves Transferability M. Terzi Alessandro Achille Marco Maggipinto Gian Antonio Susto AAML 24 23 0 22 Jul 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 37 417 0 16 Jul 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip Torr NoLa AAML 23 57 0 08 Jul 2020
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification Chen Dan Yuting Wei Pradeep Ravikumar 26 45 0 29 Jun 2020
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy Sijia Liu Gaoyuan Zhang Cynthia Liu Pin-Yu Chen Shiyu Chang Luca Daniel AAML FAtt 32 66 0 26 Jun 2020
Self-training Avoids Using Spurious Features Under Domain Shift Yining Chen Colin Wei Ananya Kumar Tengyu Ma OOD 29 85 0 17 Jun 2020
Provable tradeoffs in adversarially robust classification Yan Sun Hamed Hassani David Hong Alexander Robey 23 53 0 09 Jun 2020
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation Deepan Das Haley Massa Abhimanyu Kulkarni Theodoros Rekatsinas 29 18 0 06 Jun 2020
Estimating Principal Components under Adversarial Perturbations Pranjal Awasthi Xue Chen Aravindan Vijayaraghavan AAML 17 2 0 31 May 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 22 5 0 20 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 39 147 0 20 May 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 26 18 0 19 May 2020
Training robust neural networks using Lipschitz bounds Patricia Pauli Anne Koch J. Berberich Paul Kohler Frank Allgöwer 19 156 0 06 May 2020
Adversarial Training against Location-Optimized Adversarial Patches Sukrut Rao David Stutz Bernt Schiele AAML 19 92 0 05 May 2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness Pu Zhao Pin-Yu Chen Payel Das Karthikeyan N. Ramamurthy Xue Lin AAML 58 185 0 30 Apr 2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks Pranjal Awasthi Natalie Frank M. Mohri AAML 36 56 0 28 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 31 8 0 23 Apr 2020
Provably robust deep generative models Filipe Condessa Zico Kolter AAML OOD 14 5 0 22 Apr 2020
Adversarial Attacks on Monocular Depth Estimation Ziqi Zhang Xinge Zhu Yingwei Li Xiangqun Chen Yao Guo AAML MDE 30 25 0 23 Mar 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 99 0 20 Mar 2020
Face-Off: Adversarial Face Obfuscation Varun Chandrasekaran Chuhan Gao Brian Tang Kassem Fawaz S. Jha Suman Banerjee PICV 19 44 0 19 Mar 2020
Toward Adversarial Robustness via Semi-supervised Robust Training Yiming Li Baoyuan Wu Yan Feng Yanbo Fan Yong Jiang Zhifeng Li Shutao Xia AAML 87 13 0 16 Mar 2020
Topological Effects on Attacks Against Vertex Classification B. A. Miller Mustafa Çamurcu Alexander J. Gomez Kevin S. Chan Tina Eliassi-Rad AAML 19 2 0 12 Mar 2020
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection Mohammadreza Salehi Atrin Arya Barbod Pajoum Mohammad Otoofi Amirreza Shaeiri M. Rohban Hamid R. Rabiee AAML 29 62 0 12 Mar 2020
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods Sanghyuk Chun Seong Joon Oh Sangdoo Yun Dongyoon Han Junsuk Choe Y. Yoo AAML OOD 345 53 0 09 Mar 2020
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks Siqi Liu A. Setio Florin-Cristian Ghesu Eli Gibson Sasa Grbic Bogdan Georgescu Dorin Comaniciu AAML OOD 33 40 0 08 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 163 113 0 05 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 72 63 0 02 Mar 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 33 397 0 26 Feb 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 37 69 0 25 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang AAML 51 224 0 25 Feb 2020
MaxUp: A Simple Way to Improve Generalization of Neural Network Training Chengyue Gong Tongzheng Ren Mao Ye Qiang Liu AAML 27 56 0 20 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 106 823 0 19 Feb 2020
CAT: Customized Adversarial Training for Improved Robustness Minhao Cheng Qi Lei Pin-Yu Chen Inderjit Dhillon Cho-Jui Hsieh OOD AAML 35 114 0 17 Feb 2020
CEB Improves Model Robustness Ian S. Fischer Alexander A. Alemi AAML 19 28 0 13 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 38 64 0 11 Feb 2020
Adversarial Robustness for Code Pavol Bielik Martin Vechev AAML 22 89 0 11 Feb 2020