Instance Correction for Learning with Open-set Noisy Labels

Instance Correction for Learning with Open-set Noisy Labels

1 June 2021

Xiaobo Xia

Bo Han

Masashi Sugiyama

Papers citing "Instance Correction for Learning with Open-set Noisy Labels"

7 / 7 papers shown

Title
Open set label noise learning with robust sample selection and margin-guided module Yuandi Zhao Qianxi Xia Yang Sun Zhijie Wen Liyan Ma Shihui Ying NoLa 54 0 0 08 Jan 2025
Fairness Improves Learning from Noisily Labeled Long-Tailed Data Jiaheng Wei Zhaowei Zhu Gang Niu Tongliang Liu Sijia Liu Masashi Sugiyama Yang Liu 42 6 0 22 Mar 2023
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 215 345 0 15 Dec 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei Hangyu Liu Tongliang Liu Gang Niu Masashi Sugiyama Yang Liu NoLa 32 69 0 08 Jun 2021
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 133 121 0 04 Feb 2021
Free Lunch for Few-shot Learning: Distribution Calibration Shuo Yang Lu Liu Min Xu OODD 219 324 0 16 Jan 2021
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning Zhuo Huang Ying Tai Chengjie Wang Jian Yang Chen Gong 33 23 0 27 Nov 2020