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Interpreting Adversarially Trained Convolutional Neural Networks

Interpreting Adversarially Trained Convolutional Neural Networks

23 May 2019
Tianyuan Zhang
Zhanxing Zhu
    AAML
    GAN
    FAtt
ArXivPDFHTML

Papers citing "Interpreting Adversarially Trained Convolutional Neural Networks"

26 / 26 papers shown
Title
Theoretical Understanding of Learning from Adversarial Perturbations
Theoretical Understanding of Learning from Adversarial Perturbations
Soichiro Kumano
Hiroshi Kera
Toshihiko Yamasaki
AAML
31
1
0
16 Feb 2024
Tree Prompting: Efficient Task Adaptation without Fine-Tuning
Tree Prompting: Efficient Task Adaptation without Fine-Tuning
John X. Morris
Chandan Singh
Alexander M. Rush
Jianfeng Gao
Yuntian Deng
VLM
LRM
17
17
0
21 Oct 2023
A Comprehensive Study on Robustness of Image Classification Models:
  Benchmarking and Rethinking
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
Chang-Shu Liu
Yinpeng Dong
Wenzhao Xiang
X. Yang
Hang Su
Junyi Zhu
YueFeng Chen
Yuan He
H. Xue
Shibao Zheng
OOD
VLM
AAML
17
72
0
28 Feb 2023
Fourier Sensitivity and Regularization of Computer Vision Models
Fourier Sensitivity and Regularization of Computer Vision Models
K. Krishnamachari
See-Kiong Ng
Chuan-Sheng Foo
OOD
20
2
0
31 Jan 2023
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks
Shutong Wu
Sizhe Chen
Cihang Xie
X. Huang
AAML
40
26
0
24 May 2022
Fast AdvProp
Fast AdvProp
Jieru Mei
Yucheng Han
Yutong Bai
Yixiao Zhang
Yingwei Li
Xianhang Li
Alan Yuille
Cihang Xie
AAML
24
8
0
21 Apr 2022
Does Robustness on ImageNet Transfer to Downstream Tasks?
Does Robustness on ImageNet Transfer to Downstream Tasks?
Yutaro Yamada
Mayu Otani
OOD
18
27
0
08 Apr 2022
A Survey of Robust Adversarial Training in Pattern Recognition:
  Fundamental, Theory, and Methodologies
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies
Zhuang Qian
Kaizhu Huang
Qiufeng Wang
Xu-Yao Zhang
OOD
AAML
ObjD
47
71
0
26 Mar 2022
Pareto Adversarial Robustness: Balancing Spatial Robustness and
  Sensitivity-based Robustness
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Ke Sun
Mingjie Li
Zhouchen Lin
AAML
19
2
0
03 Nov 2021
Provably Efficient Black-Box Action Poisoning Attacks Against
  Reinforcement Learning
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning
Guanlin Liu
Lifeng Lai
AAML
30
34
0
09 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A
  survey
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Ajmal Saeed Mian
Navid Kardan
M. Shah
AAML
26
235
0
01 Aug 2021
Built-in Elastic Transformations for Improved Robustness
Built-in Elastic Transformations for Improved Robustness
Sadaf Gulshad
Ivan Sosnovik
A. Smeulders
AAML
22
1
0
20 Jul 2021
Attack to Fool and Explain Deep Networks
Attack to Fool and Explain Deep Networks
Naveed Akhtar
M. Jalwana
Bennamoun
Ajmal Saeed Mian
AAML
14
33
0
20 Jun 2021
Partial success in closing the gap between human and machine vision
Partial success in closing the gap between human and machine vision
Robert Geirhos
Kantharaju Narayanappa
Benjamin Mitzkus
Tizian Thieringer
Matthias Bethge
Felix Wichmann
Wieland Brendel
VLM
AAML
40
221
0
14 Jun 2021
Impact of Spatial Frequency Based Constraints on Adversarial Robustness
Impact of Spatial Frequency Based Constraints on Adversarial Robustness
Rémi Bernhard
Pierre-Alain Moëllic
Martial Mermillod
Yannick Bourrier
Romain Cohendet
M. Solinas
M. Reyboz
AAML
19
16
0
26 Apr 2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness
A Unified Game-Theoretic Interpretation of Adversarial Robustness
Jie Ren
Die Zhang
Yisen Wang
Lu Chen
Zhanpeng Zhou
...
Xu Cheng
Xin Eric Wang
Meng Zhou
Jie Shi
Quanshi Zhang
AAML
64
22
0
12 Mar 2021
Certified Monotonic Neural Networks
Certified Monotonic Neural Networks
Xingchao Liu
Xing Han
Na Zhang
Qiang Liu
16
78
0
20 Nov 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural
  Networks
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks
Tao Bai
Jinqi Luo
Jun Zhao
AAML
41
8
0
03 Nov 2020
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image
  Classification
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
Oren Nuriel
Sagie Benaim
Lior Wolf
28
88
0
09 Oct 2020
Do Adversarially Robust ImageNet Models Transfer Better?
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman
Andrew Ilyas
Logan Engstrom
Ashish Kapoor
A. Madry
32
416
0
16 Jul 2020
Adversarial Attacks and Defenses: An Interpretation Perspective
Adversarial Attacks and Defenses: An Interpretation Perspective
Ninghao Liu
Mengnan Du
Ruocheng Guo
Huan Liu
Xia Hu
AAML
24
8
0
23 Apr 2020
The Curious Case of Adversarially Robust Models: More Data Can Help,
  Double Descend, or Hurt Generalization
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization
Yifei Min
Lin Chen
Amin Karbasi
AAML
26
69
0
25 Feb 2020
Universal Adversarial Attack on Attention and the Resulting Dataset
  DAmageNet
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
Sizhe Chen
Zhengbao He
Chengjin Sun
Jie-jin Yang
Xiaolin Huang
AAML
21
103
0
16 Jan 2020
Adversarial Examples in Modern Machine Learning: A Review
Adversarial Examples in Modern Machine Learning: A Review
R. Wiyatno
Anqi Xu
Ousmane Amadou Dia
A. D. Berker
AAML
13
103
0
13 Nov 2019
Constructing Unrestricted Adversarial Examples with Generative Models
Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song
Rui Shu
Nate Kushman
Stefano Ermon
GAN
AAML
174
302
0
21 May 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
263
5,833
0
08 Jul 2016
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