Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels

2 December 2020

Xiaobo Xia

Bo Han

Nannan Wang

Papers citing "Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels"

6 / 6 papers shown

Title
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration Manyi Zhang Yuxin Ren Zihao W. Wang C. Yuan 21 3 0 11 Oct 2022
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels Ganlong Zhao Guanbin Li Yipeng Qin Feng Liu Yizhou Yu NoLa 22 22 0 29 Jul 2022
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation Xiaoqing Guo Jie Liu Tongliang Liu Yiyuan Yuan 30 27 0 29 Mar 2022
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
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 20 23 0 27 Nov 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 313 497 0 05 Mar 2020