ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1909.03013
  4. Cited By
Approaching Machine Learning Fairness through Adversarial Network

Approaching Machine Learning Fairness through Adversarial Network

6 September 2019
Xiaoqian Wang
Heng-Chiao Huang
    FaML
ArXiv (abs)PDFHTML

Papers citing "Approaching Machine Learning Fairness through Adversarial Network"

5 / 5 papers shown
Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent
  Circumvention
Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent CircumventionConference on Fairness, Accountability and Transparency (FAccT), 2024
Cedric Deslandes Whitney
Justin Norman
324
55
0
03 May 2024
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing (JRC), 2022
Max Hort
Zhenpeng Chen
Jie M. Zhang
Mark Harman
Federica Sarro
FaMLAI4CE
435
266
0
14 Jul 2022
Fairness via Representation Neutralization
Fairness via Representation Neutralization
Mengnan Du
Subhabrata Mukherjee
Guanchu Wang
Ruixiang Tang
Ahmed Hassan Awadallah
Helen Zhou
392
87
0
23 Jun 2021
FAIR: Fair Adversarial Instance Re-weighting
FAIR: Fair Adversarial Instance Re-weightingNeurocomputing (Neurocomputing), 2020
Andrija Petrović
Mladen Nikolic
Sandro Radovanović
Boris Delibavsić
Milovs Jovanović
FaMLAAML
212
42
0
15 Nov 2020
Exploring Racial Bias within Face Recognition via per-subject
  Adversarially-Enabled Data Augmentation
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
Seyma Yucer
S. Akçay
Noura Al-Moubayed
T. Breckon
234
70
0
19 Apr 2020
1
Page 1 of 1