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Towards Evaluating the Robustness of Neural Networks Learned by
  Transduction

Towards Evaluating the Robustness of Neural Networks Learned by Transduction

27 October 2021
Jiefeng Chen
Xi Wu
Yang Guo
Yingyu Liang
S. Jha
    ELM
    AAML
ArXivPDFHTML

Papers citing "Towards Evaluating the Robustness of Neural Networks Learned by Transduction"

5 / 5 papers shown
Title
Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost
Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost
Cheng-Han Yeh
Kuanchun Yu
Chun-Shien Lu
DiffM
AAML
38
0
0
22 Oct 2024
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
Yongyi Su
Yushu Li
Nanqing Liu
Kui Jia
Xulei Yang
Chuan-Sheng Foo
Xun Xu
TTA
AAML
61
1
0
07 Oct 2024
LAS-AT: Adversarial Training with Learnable Attack Strategy
LAS-AT: Adversarial Training with Learnable Attack Strategy
Xiaojun Jia
Yong Zhang
Baoyuan Wu
Ke Ma
Jue Wang
Xiaochun Cao
AAML
47
131
0
13 Mar 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Francesco Croce
Sven Gowal
T. Brunner
Evan Shelhamer
Matthias Hein
A. Cemgil
TTA
AAML
181
68
0
28 Feb 2022
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
317
5,847
0
08 Jul 2016
1