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Disentangling Adversarial Robustness and Generalization

3 December 2018

David Stutz

Matthias Hein

Bernt Schiele

AAML

OOD

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Papers citing "Disentangling Adversarial Robustness and Generalization"

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Title
Generalized Real-World Super-Resolution through Adversarial Robustness Angela Castillo María Escobar Juan C. Pérez Andrés Romero Radu Timofte Luc Van Gool Pablo Arbelaez 77 16 0 25 Aug 2021
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE Wenzhao Xiang Chang-rui Liu Shibao Zheng 44 2 0 25 Aug 2021
Semantic Perturbations with Normalizing Flows for Improved Generalization Oğuz Kaan Yüksel Sebastian U. Stich Martin Jaggi Tatjana Chavdarova AAML 79 10 0 18 Aug 2021
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach Xi Peng Fengchun Qiao Long Zhao OOD 126 36 0 05 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 162 241 0 01 Aug 2021
Adversarial Attacks with Time-Scale Representations Alberto Santamaria-Pang Jia-dong Qiu Aritra Chowdhury James R. Kubricht Peter Tu Iyer Naresh Nurali Virani AAML MLAU 47 0 0 26 Jul 2021
Uncertainty-Aware Reliable Text Classification Yibo Hu Latifur Khan EDL UQCV 74 33 0 15 Jul 2021
A Closer Look at the Adversarial Robustness of Information Bottleneck Models I. Korshunova David Stutz Alexander A. Alemi Olivia Wiles Sven Gowal 49 3 0 12 Jul 2021
GAN-based Data Augmentation for Chest X-ray Classification Shobhita Sundaram Neha Hulkund MedIm 77 36 0 07 Jul 2021
On Generalization of Graph Autoencoders with Adversarial Training Tianjin Huang Yulong Pei Vlado Menkovski Mykola Pechenizkiy GNN 119 6 0 06 Jul 2021
Adversarial Examples Make Strong Poisons Liam H. Fowl Micah Goldblum Ping Yeh-Chiang Jonas Geiping Wojtek Czaja Tom Goldstein SILM 105 136 0 21 Jun 2021
The Dimpled Manifold Model of Adversarial Examples in Machine Learning A. Shamir Odelia Melamed Oriel BenShmuel AAML 96 50 0 18 Jun 2021
Attacking Adversarial Attacks as A Defense Boxi Wu Heng Pan Li Shen Jindong Gu Shuai Zhao Zhifeng Li Deng Cai Xiaofei He Wei Liu AAML 93 32 0 09 Jun 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 80 44 0 03 Jun 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang Xilie Xu Bo Han Tongliang Liu Gang Niu Li-zhen Cui Masashi Sugiyama NoLa AAML 87 9 0 31 May 2021
Adversarial Robustness against Multiple and Single $l_p$ -Threat Models via Quick Fine-Tuning of Robust Classifiers Francesco Croce Matthias Hein OOD AAML 67 18 0 26 May 2021
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks Dequan Wang An Ju Evan Shelhamer David Wagner Trevor Darrell AAML 119 27 0 18 May 2021
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 247 273 0 18 Apr 2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators David Stutz Nandhini Chandramoorthy Matthias Hein Bernt Schiele AAML MQ 68 18 0 16 Apr 2021
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach Simiao Zuo Chen Liang Haoming Jiang Xiaodong Liu Pengcheng He Jianfeng Gao Weizhu Chen T. Zhao 116 9 0 11 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 105 67 0 09 Apr 2021
Adversarial Feature Augmentation and Normalization for Visual Recognition Tianlong Chen Yu Cheng Zhe Gan Jianfeng Wang Lijuan Wang Zhangyang Wang Jingjing Liu AAML ViT 71 19 0 22 Mar 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 98 34 0 15 Mar 2021
Uncertainty-guided Model Generalization to Unseen Domains Fengchun Qiao Xi Peng OOD UQCV 90 61 0 12 Mar 2021
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples Washington Garcia Pin-Yu Chen S. Jha Scott Clouse Kevin R. B. Butler AAML 43 0 0 04 Mar 2021
On the effectiveness of adversarial training against common corruptions Klim Kireev Maksym Andriushchenko Nicolas Flammarion AAML 72 103 0 03 Mar 2021
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning Chia-Yi Hsu Pin-Yu Chen Songtao Lu Sijia Liu Chia-Mu Yu AAML 91 11 0 02 Mar 2021
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective Tianlong Chen Yu Cheng Zhe Gan Jingjing Liu Zhangyang Wang 82 52 0 28 Feb 2021
Generating Structured Adversarial Attacks Using Frank-Wolfe Method Ehsan Kazemi Thomas Kerdreux Liquang Wang AAML DiffM 48 1 0 15 Feb 2021
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 56 13 0 09 Feb 2021
Noisy Recurrent Neural Networks Soon Hoe Lim N. Benjamin Erichson Liam Hodgkinson Michael W. Mahoney 93 54 0 09 Feb 2021
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao Bihan Wen Qian Wang AAML 192 496 0 02 Feb 2021
Online Adversarial Purification based on Self-Supervision Changhao Shi Chester Holtz Zhengchao Wan AAML 82 57 0 23 Jan 2021
Analysis of Dominant Classes in Universal Adversarial Perturbations Jon Vadillo Roberto Santana Jose A. Lozano AAML 54 5 0 28 Dec 2020
A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning Ahmadreza Jeddi M. Shafiee A. Wong AAML 82 40 0 25 Dec 2020
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks L. N. Darlow Stanisław Jastrzębski Amos Storkey 134 24 0 19 Nov 2020
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty Camilo Pestana Wei Liu D. Glance Ajmal Mian AAML 121 5 0 05 Nov 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 87 8 0 03 Nov 2020
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data Lingkai Kong Haoming Jiang Yuchen Zhuang Jie Lyu T. Zhao Chao Zhang OODD 91 26 0 22 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 95 23 0 15 Oct 2020
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks Wei-An Lin Chun Pong Lau Alexander Levine Ramalingam Chellappa Soheil Feizi AAML 121 60 0 05 Sep 2020
Trace-Norm Adversarial Examples Ehsan Kazemi Thomas Kerdreux Liqiang Wang 59 2 0 02 Jul 2020
Bit Error Robustness for Energy-Efficient DNN Accelerators David Stutz Nandhini Chandramoorthy Matthias Hein Bernt Schiele MQ 52 1 0 24 Jun 2020
A general framework for defining and optimizing robustness Alessandro Tibo M. Jaeger Kim G. Larsen 22 0 0 19 Jun 2020
Calibrated neighborhood aware confidence measure for deep metric learning Maryna Karpusha Sunghee Yun István Fehérvári UQCV FedML 121 2 0 08 Jun 2020
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing Vishaal Krishnan Abed AlRahman Al Makdah Fabio Pasqualetti OOD AAML 78 23 0 05 Jun 2020
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology Sharon Zhou E. Zelikman F. Lu A. Ng Gunnar Carlsson Stefano Ermon DRL 61 27 0 05 Jun 2020
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds Kibok Lee Zhuoyuan Chen Xinchen Yan R. Urtasun Ersin Yumer 3DPC 66 32 0 24 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 122 151 0 20 May 2020
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data Lu Wang Huan Zhang Jinfeng Yi Cho-Jui Hsieh Yuan Jiang AAML 82 12 0 11 May 2020