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Distilling Adversarial Robustness Using Heterogeneous Teachers

Distilling Adversarial Robustness Using Heterogeneous Teachers

23 February 2024
Jieren Deng
A. Palmer
Rigel Mahmood
Ethan Rathbun
Jinbo Bi
Kaleel Mahmood
Derek Aguiar
    AAML
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Papers citing "Distilling Adversarial Robustness Using Heterogeneous Teachers"

3 / 3 papers shown
Title
On the Convergence and Robustness of Adversarial Training
On the Convergence and Robustness of Adversarial Training
Yisen Wang
Xingjun Ma
James Bailey
Jinfeng Yi
Bowen Zhou
Quanquan Gu
AAML
192
345
0
15 Dec 2021
Recent Advances in Adversarial Training for Adversarial Robustness
Recent Advances in Adversarial Training for Adversarial Robustness
Tao Bai
Jinqi Luo
Jun Zhao
B. Wen
Qian Wang
AAML
71
473
0
02 Feb 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
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