Fairness Improves Learning from Noisily Labeled Long-Tailed Data

Fairness Improves Learning from Noisily Labeled Long-Tailed Data

22 March 2023

Zhaowei Zhu

Masashi Sugiyama

Yang Liu

Papers citing "Fairness Improves Learning from Noisily Labeled Long-Tailed Data"

11 / 11 papers shown

Title
Unprocessing Seven Years of Algorithmic Fairness André F. Cruz Moritz Hardt 16 10 0 12 Jun 2023
Mitigating Source Bias for Fairer Weak Supervision Changho Shin Sonia Cromp Dyah Adila Frederic Sala 9 2 0 30 Mar 2023
Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes Zhaowei Zhu Yuanshun Yao Jiankai Sun Hanguang Li Y. Liu 4 21 0 06 Oct 2022
To Aggregate or Not? Learning with Separate Noisy Labels Jiaheng Wei Zhaowei Zhu Tianyi Luo Ehsan Amid Abhishek Kumar Yang Liu NoLa 15 37 0 14 Jun 2022
Mitigating Neural Network Overconfidence with Logit Normalization Hongxin Wei Renchunzi Xie Hao-Ran Cheng Lei Feng Bo An Yixuan Li OODD 160 258 0 19 May 2022
Identifiability of Label Noise Transition Matrix Yang Liu Hao Cheng Kun Zhang NoLa 17 31 0 04 Feb 2022
Detecting Corrupted Labels Without Training a Model to Predict Zhaowei Zhu Zihao Dong Yang Liu NoLa 135 61 0 12 Oct 2021
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Zhaowei Zhu Tianyi Luo Yang Liu 145 39 0 12 Oct 2021
Influence-Balanced Loss for Imbalanced Visual Classification Seulki Park Jongin Lim Younghan Jeon J. Choi CVBM 76 129 0 06 Oct 2021
Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren Cunjun Yu Shunan Sheng Xiao Ma Haiyu Zhao Shuai Yi Hongsheng Li 152 541 0 21 Jul 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 488 0 05 Mar 2020