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Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors

21 June 2024

Papers citing "Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors"

50 / 102 papers shown

Title
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu Yutong Lin Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin B. Guo ViT 453 21,439 0 25 Mar 2021
Learning Transferable Visual Models From Natural Language Supervision Alec Radford Jong Wook Kim Chris Hallacy Aditya A. Ramesh Gabriel Goh ... Amanda Askell Pamela Mishkin Jack Clark Gretchen Krueger Ilya Sutskever CLIP VLM 931 29,436 0 26 Feb 2021
RepVGG: Making VGG-style ConvNets Great Again Xiaohan Ding Xinming Zhang Ningning Ma Jungong Han Guiguang Ding Jian Sun 284 1,599 0 11 Jan 2021
Training data-efficient image transformers & distillation through attention Hugo Touvron Matthieu Cord Matthijs Douze Francisco Massa Alexandre Sablayrolles Hervé Jégou ViT 387 6,768 0 23 Dec 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai ... Matthias Minderer G. Heigold Sylvain Gelly Jakob Uszkoreit N. Houlsby ViT 657 41,103 0 22 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 322 702 0 19 Oct 2020
Weight-Covariance Alignment for Adversarially Robust Neural Networks Panagiotis Eustratiadis Henry Gouk Da Li Timothy M. Hospedales OOD AAML 61 23 0 17 Oct 2020
Energy-based Out-of-distribution Detection Weitang Liu Xiaoyun Wang John Douglas Owens Yixuan Li OODD 271 1,356 0 08 Oct 2020
Open-set Adversarial Defense Rui Shao Pramuditha Perera Pong C. Yuen Vishal M. Patel AAML 105 32 0 02 Sep 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 76 37 0 13 Jul 2020
A Critical Evaluation of Open-World Machine Learning Liwei Song Vikash Sehwag A. Bhagoji Prateek Mittal AAML 46 9 0 08 Jul 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 117 546 0 01 Jul 2020
ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 50 139 0 26 Jun 2020
Towards Understanding Fast Adversarial Training Bai Li Shiqi Wang Suman Jana Lawrence Carin AAML 66 50 0 04 Jun 2020
Designing Network Design Spaces Ilija Radosavovic Raj Prateek Kosaraju Ross B. Girshick Kaiming He Piotr Dollár GNN 102 1,682 0 30 Mar 2020
TResNet: High Performance GPU-Dedicated Architecture T. Ridnik Hussam Lawen Asaf Noy Emanuel Ben-Baruch Gilad Sharir Itamar Friedman OOD 78 213 0 30 Mar 2020
Robust Out-of-distribution Detection for Neural Networks Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 192 87 0 21 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 221 1,846 0 03 Mar 2020
Why is the Mahalanobis Distance Effective for Anomaly Detection? Ryo Kamoi Kei Kobayashi OODD 169 58 0 01 Mar 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 277 834 0 19 Feb 2020
Adversarial Distributional Training for Robust Deep Learning Yinpeng Dong Zhijie Deng Tianyu Pang Hang Su Jun Zhu OOD 71 122 0 14 Feb 2020
Big Transfer (BiT): General Visual Representation Learning Alexander Kolesnikov Lucas Beyer Xiaohua Zhai J. Puigcerver Jessica Yung Sylvain Gelly N. Houlsby MQ 286 1,205 0 24 Dec 2019
Scaling Out-of-Distribution Detection for Real-World Settings Dan Hendrycks Steven Basart Mantas Mazeika Andy Zou Joe Kwon Mohammadreza Mostajabi Jacob Steinhardt Basel Alomair OODD 180 481 0 25 Nov 2019
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Mehdi Neshat Zifan Wang Bradley Alexander Fan Yang Zijian Zhang Sirui Ding Markus Wagner Xia Hu FAtt 93 1,069 0 03 Oct 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt Basel Alomair OODD 212 1,472 0 16 Jul 2019
Fixing the train-test resolution discrepancy Hugo Touvron Andrea Vedaldi Matthijs Douze Hervé Jégou 119 423 0 14 Jun 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 65 26 0 05 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 177 3,435 0 28 Mar 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 89 901 0 18 Feb 2019
Attention, Please! Adversarial Defense via Activation Rectification and Preservation Shangxi Wu Jitao Sang Kaiyuan Xu Jiaming Zhang Jian Yu AAML 26 7 0 24 Nov 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 187 2,051 0 10 Jul 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 230 3,186 0 01 Feb 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 184 19,284 0 13 Jan 2018
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 157 2,153 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,069 0 19 Jun 2017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks Shiyu Liang Yixuan Li R. Srikant UQCV OODD 168 2,072 0 08 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 126 1,864 0 20 May 2017
Grad-CAM: Why did you say that? Ramprasaath R. Selvaraju Abhishek Das Ramakrishna Vedantam Michael Cogswell Devi Parikh Dhruv Batra FAtt 70 473 0 22 Nov 2016
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu Basel Alomair AAML 140 1,737 0 08 Nov 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 321 20,023 0 07 Oct 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 164 3,454 0 07 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 775 36,813 0 25 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 543 5,897 0 08 Jul 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 349 7,985 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 354 10,184 0 16 Mar 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,678 0 08 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 250 9,326 0 14 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 151 4,897 0 14 Nov 2015