Removing Undesirable Feature Contributions Using Out-of-Distribution Data

17 January 2021

Papers citing "Removing Undesirable Feature Contributions Using Out-of-Distribution Data"

39 / 39 papers shown

Title
Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models Shuoyuan Wang Yixuan Li Hongxin Wei VLM 99 2 0 03 Oct 2024
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 152 102 0 20 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 193 115 0 05 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 211 1,837 0 03 Mar 2020
Hold me tight! Influence of discriminative features on deep network boundaries Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 32 50 0 15 Feb 2020
What it Thinks is Important is Important: Robustness Transfers through Input Gradients Alvin Chan Yi Tay Yew-Soon Ong AAML OOD 43 51 0 11 Dec 2019
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 75 987 0 29 Nov 2019
Self-training with Noisy Student improves ImageNet classification Qizhe Xie Minh-Thang Luong Eduard H. Hovy Quoc V. Le NoLa 288 2,387 0 11 Nov 2019
Learning De-biased Representations with Biased Representations Hyojin Bahng Sanghyuk Chun Sangdoo Yun Jaegul Choo Seong Joon Oh OOD 372 279 0 07 Oct 2019
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Francesco Croce Matthias Hein AAML 84 488 0 03 Jul 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 119 751 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 68 333 0 31 May 2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks Haohan Wang Xindi Wu Pengcheng Yin Eric Xing 57 521 0 28 May 2019
Cross-Domain Transferability of Adversarial Perturbations Muzammal Naseer Salman H. Khan M. H. Khan Fahad Shahbaz Khan Fatih Porikli AAML 78 145 0 28 May 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi S. Maeda Masanori Koyama Takeru Miyato OOD 75 130 0 24 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 604 4,766 0 13 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 87 1,836 0 06 May 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 67 726 0 28 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 127 2,542 0 24 Jan 2019
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 151 557 0 13 Dec 2018
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 165 1,475 0 11 Dec 2018
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Robert Geirhos Patricia Rubisch Claudio Michaelis Matthias Bethge Felix Wichmann Wieland Brendel 96 2,662 0 29 Nov 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 93 1,776 0 30 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 131 789 0 30 Apr 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 95 628 0 16 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 189 3,180 0 01 Feb 2018
Generative Adversarial Perturbations Omid Poursaeed Isay Katsman Bicheng Gao Serge J. Belongie AAML GAN WIGM 64 355 0 06 Dec 2017
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee Honglak Lee Kibok Lee Jinwoo Shin OODD 113 881 0 26 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 271 9,743 0 25 Oct 2017
VisDA: The Visual Domain Adaptation Challenge Xingchao Peng Ben Usman Neela Kaushik Judy Hoffman Dequan Wang Kate Saenko OOD 81 800 0 18 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 271 12,029 0 19 Jun 2017
Good Semi-supervised Learning that Requires a Bad GAN Zihang Dai Zhilin Yang Fan Yang William W. Cohen Ruslan Salakhutdinov GAN 45 483 0 27 May 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 316 4,624 0 10 Nov 2016
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 130 2,525 0 26 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 228 8,548 0 16 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 314 7,971 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 332 10,172 0 16 Mar 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 235 19,017 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 241 14,893 1 21 Dec 2013