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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
Ke Sun
Zhanxing Zhu
Zhouchen Lin
    AAML
ArXiv (abs)PDFHTML

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

11 / 11 papers shown
Machine Learning Robustness: A Primer
Machine Learning Robustness: A Primer
Houssem Ben Braiek
Foutse Khomh
AAMLOOD
467
21
0
01 Apr 2024
A.I. Robustness: a Human-Centered Perspective on Technological
  Challenges and Opportunities
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
321
25
0
17 Oct 2022
UQGAN: A Unified Model for Uncertainty Quantification of Deep
  Classifiers trained via Conditional GANs
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
392
23
0
31 Jan 2022
BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples
BOSS: Bidirectional One-Shot Synthesis of Adversarial ExamplesInternational Workshop on Machine Learning for Signal Processing (MLSP), 2021
Ismail Alkhouri
Alvaro Velasquez
George Atia
AAMLGAN
130
1
0
05 Aug 2021
ATRO: Adversarial Training with a Rejection Option
ATRO: Adversarial Training with a Rejection Option
Masahiro Kato
Zhenghang Cui
Yoshihiro Fukuhara
AAML
171
11
0
24 Oct 2020
Medical Image Harmonization Using Deep Learning Based Canonical Mapping:
  Toward Robust and Generalizable Learning in Imaging
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
183
18
0
11 Oct 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature
  Space
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
Riccardo Guidotti
A. Monreale
Stan Matwin
D. Pedreschi
FAtt
212
70
0
27 Jan 2020
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial
  Examples
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
Wanting Yu
Hongyi Yu
Lingyun Jiang
Mengli Zhang
Kai Qiao
GANAAML
293
0
0
17 Sep 2019
Diminishing the Effect of Adversarial Perturbations via Refining Feature
  Representation
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation
Nader Asadi
Amirm. Sarfi
Mehrdad Hosseinzadeh
Sahba Tahsini
M. Eftekhari
AAML
130
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
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
169
7
0
25 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal
  Principle
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
358
384
0
02 May 2019
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