Enhancing the Robustness of Deep Neural Networks by Boundary Conditional
GAN

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN

28 February 2019

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Papers citing "Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN"

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Title
Machine Learning Robustness: A Primer Houssem Ben Braiek Foutse Khomh AAML OOD 347 20 0 01 Apr 2024
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and OpportunitiesACM Computing Surveys (ACM CSUR), 2022 Andrea Tocchetti Lorenzo Corti Agathe Balayn Mireia Yurrita Philip Lippmann Marco Brambilla Jie Yang 293 23 0 17 Oct 2022
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANsNeural Information Processing Systems (NeurIPS), 2022 Philipp Oberdiek G. Fink Matthias Rottmann OODD 328 22 0 31 Jan 2022
BOSS: Bidirectional One-Shot Synthesis of Adversarial ExamplesInternational Workshop on Machine Learning for Signal Processing (MLSP), 2021 Ismail Alkhouri Alvaro Velasquez George Atia AAML GAN 94 1 0 05 Aug 2021
ATRO: Adversarial Training with a Rejection Option Masahiro Kato Zhenghang Cui Yoshihiro Fukuhara AAML 159 11 0 24 Oct 2020
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging V. Bashyam J. Doshi G. Erus D. Srinivasan Ahmed Abdulkadir ... R. Nick David A. Wolk H. Shou I. Nasrallah Christos Davatzikos MedIm 143 18 0 11 Oct 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 204 70 0 27 Jan 2020
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples Wanting Yu Hongyi Yu Lingyun Jiang Mengli Zhang Kai Qiao GAN AAML 285 0 0 17 Sep 2019
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation Nader Asadi Amirm. Sarfi Mehrdad Hosseinzadeh Sahba Tahsini M. Eftekhari AAML 118 2 0 01 Jul 2019
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks Xiaodong Wu Hui Xie Leixin Zhou Xiaodong Wu Weiyu Xu R. Mudumbai AAML 141 7 0 25 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal PrincipleNeural Information Processing Systems (NeurIPS), 2019 Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 278 383 0 02 May 2019