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Label Noise in Adversarial Training: A Novel Perspective to Study Robust
  Overfitting

Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting

7 October 2021
Chengyu Dong
Liyuan Liu
Jingbo Shang
    NoLa
    AAML
ArXivPDFHTML

Papers citing "Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting"

4 / 4 papers shown
Title
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better
MingWei Zhou
Xiaobing Pei
AAML
68
0
0
30 Mar 2025
Omnipotent Adversarial Training in the Wild
Omnipotent Adversarial Training in the Wild
Guanlin Li
Kangjie Chen
Yuan Xu
Han Qiu
Tianwei Zhang
14
0
0
14 Jul 2023
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
256
3,102
0
04 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
273
2,878
0
15 Sep 2016
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