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Adversarial Perturbations Are Not So Weird: Entanglement of Robust and
  Non-Robust Features in Neural Network Classifiers

Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers

9 February 2021
Jacob Mitchell Springer
Melanie Mitchell
Garrett Kenyon
    AAML
ArXiv (abs)PDFHTML

Papers citing "Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers"

5 / 5 papers shown
Theoretical Understanding of Learning from Adversarial Perturbations
Theoretical Understanding of Learning from Adversarial Perturbations
Soichiro Kumano
Hiroshi Kera
Toshihiko Yamasaki
AAML
210
4
0
16 Feb 2024
Adversarial Pixel Restoration as a Pretext Task for Transferable
  Perturbations
Adversarial Pixel Restoration as a Pretext Task for Transferable PerturbationsBritish Machine Vision Conference (BMVC), 2022
H. Malik
Shahina Kunhimon
Muzammal Naseer
Salman Khan
Fahad Shahbaz Khan
AAML
203
8
0
18 Jul 2022
Why adversarial training can hurt robust accuracy
Why adversarial training can hurt robust accuracyInternational Conference on Learning Representations (ICLR), 2022
Jacob Clarysse
Julia Hörrmann
Fanny Yang
AAML
230
22
0
03 Mar 2022
A Little Robustness Goes a Long Way: Leveraging Robust Features for
  Targeted Transfer Attacks
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer AttacksNeural Information Processing Systems (NeurIPS), 2021
Jacob Mitchell Springer
Melanie Mitchell
Garrett Kenyon
AAML
284
49
0
03 Jun 2021
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable ConvolutionsComputer Vision and Pattern Recognition (CVPR), 2016
François Chollet
MDEBDLPINN
3.0K
16,661
0
07 Oct 2016
1