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Improving Adversarial Robust Fairness via Anti-Bias Soft Label
  Distillation

Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation

9 December 2023
Shiji Zhao
Xizhe Wang
Xingxing Wei
ArXivPDFHTML

Papers citing "Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation"

3 / 3 papers shown
Title
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
Tejaswini Medi
Steffen Jung
M. Keuper
AAML
44
3
0
30 Oct 2024
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher
  Adversarial Distillation
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation
Shiji Zhao
Xizhe Wang
Xingxing Wei
AAML
48
8
0
28 Jun 2023
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg
3DV
345
75,888
0
18 May 2015
1