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A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via
  Adversarial Fine-tuning

A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning

25 December 2020
Ahmadreza Jeddi
M. Shafiee
A. Wong
    AAML
ArXivPDFHTML

Papers citing "A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning"

6 / 6 papers shown
Title
Hierarchical Distribution-Aware Testing of Deep Learning
Hierarchical Distribution-Aware Testing of Deep Learning
Wei Huang
Xingyu Zhao
Alec Banks
V. Cox
Xiaowei Huang
OOD
AAML
28
10
0
17 May 2022
Joint rotational invariance and adversarial training of a dual-stream
  Transformer yields state of the art Brain-Score for Area V4
Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4
William Berrios
Arturo Deza
MedIm
ViT
17
13
0
08 Mar 2022
ROPUST: Improving Robustness through Fine-tuning with Photonic
  Processors and Synthetic Gradients
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients
Alessandro Cappelli
Julien Launay
Laurent Meunier
Ruben Ohana
Iacopo Poli
AAML
10
4
0
06 Jul 2021
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve
  Adversarial Robustness
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi
M. Shafiee
Michelle Karg
C. Scharfenberger
A. Wong
OOD
AAML
50
63
0
02 Mar 2020
Disentangling Adversarial Robustness and Generalization
Disentangling Adversarial Robustness and Generalization
David Stutz
Matthias Hein
Bernt Schiele
AAML
OOD
186
272
0
03 Dec 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
272
5,833
0
08 Jul 2016
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