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Addressing Neural Network Robustness with Mixup and Targeted Labeling
  Adversarial Training

Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training

19 August 2020
Alfred Laugros
A. Caplier
Matthieu Ospici
    AAML
ArXivPDFHTML

Papers citing "Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training"

5 / 5 papers shown
Title
Robustmix: Improving Robustness by Regularizing the Frequency Bias of
  Deep Nets
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets
Jonas Ngnawé
Marianne Abémgnigni Njifon
Jonathan Heek
Yann N. Dauphin
OOD
8
4
0
06 Apr 2023
A Systematic Review of Robustness in Deep Learning for Computer Vision:
  Mind the gap?
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
Nathan G. Drenkow
Numair Sani
I. Shpitser
Mathias Unberath
16
73
0
01 Dec 2021
Aggregated Residual Transformations for Deep Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie
Ross B. Girshick
Piotr Dollár
Z. Tu
Kaiming He
261
10,106
0
16 Nov 2016
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
256
3,102
0
04 Nov 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,813
0
08 Jul 2016
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