Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

5 October 2020

Zhaowei Zhu

Yang Liu

Papers citing "Learning with Instance-Dependent Label Noise: A Sample Sieve Approach"

34 / 34 papers shown

Title
Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data Weiran Pan Wei Wei Feida Zhu Yong Deng NoLa 139 0 0 24 Apr 2025
Early Stopping Against Label Noise Without Validation Data Suqin Yuan Lei Feng Tongliang Liu NoLa 96 14 0 11 Feb 2025
Effective and Robust Adversarial Training against Data and Label Corruptions Pengfei Zhang Zi Huang Xin-Shun Xu Guangdong Bai 43 4 0 07 May 2024
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations Heewon Kim Hyun Sung Chang Kiho Cho Jaeyun Lee Bohyung Han NoLa 21 2 0 09 Jan 2024
Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization Shiqi Wang Yeqin Zhang Cam-Tu Nguyen 20 2 0 30 Dec 2023
Towards Reliable Dermatology Evaluation Benchmarks Fabian Gröger Simone Lionetti Philippe Gottfrois Alvaro Gonzalez-Jimenez Matthew Groh Roxana Daneshjou Labelling Consortium A. Navarini M. Pouly 19 5 0 13 Sep 2023
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels Chuanyan Hu Shipeng Yan Zhitong Gao Xuming He NoLa 19 4 0 20 Jun 2023
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation Arpit Garg Cuong C. Nguyen Rafael Felix Thanh-Toan Do G. Carneiro NoLa 28 1 0 31 May 2023
Mitigating Label Noise through Data Ambiguation Julian Lienen Eyke Hüllermeier NoLa 32 6 0 23 May 2023
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations Hao Chen Ankit Shah Jindong Wang R. Tao Yidong Wang Xingxu Xie Masashi Sugiyama Rita Singh Bhiksha Raj 29 12 0 22 May 2023
Fairness Improves Learning from Noisily Labeled Long-Tailed Data Jiaheng Wei Zhaowei Zhu Gang Niu Tongliang Liu Sijia Liu Masashi Sugiyama Yang Liu 28 6 0 22 Mar 2023
When Source-Free Domain Adaptation Meets Learning with Noisy Labels L. Yi Gezheng Xu Pengcheng Xu Jiaqi Li Ruizhi Pu Charles X. Ling A. McLeod Boyu Wang 21 39 0 31 Jan 2023
Improve Noise Tolerance of Robust Loss via Noise-Awareness Kehui Ding Jun Shu Deyu Meng Zongben Xu NoLa 19 5 0 18 Jan 2023
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification De-Chun Cheng Haichun Tai N. Wang Zhen Wang Xinbo Gao 27 3 0 30 Nov 2022
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels Daehwan Kim Kwang-seok Ryoo Hansang Cho Seung Wook Kim NoLa 24 3 0 20 Nov 2022
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
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State Xinshao Wang Yang Hua Elyor Kodirov S. Mukherjee David A. Clifton N. Robertson 15 6 0 30 Jun 2022
Training Subset Selection for Weak Supervision Hunter Lang Aravindan Vijayaraghavan David Sontag NoLa 8 21 0 06 Jun 2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation De-Chun Cheng Tongliang Liu Yixiong Ning Nannan Wang Bo Han Gang Niu Xinbo Gao Masashi Sugiyama NoLa 37 65 0 06 Jun 2022
Selective-Supervised Contrastive Learning with Noisy Labels Shikun Li Xiaobo Xia Shiming Ge Tongliang Liu NoLa 19 172 0 08 Mar 2022
Dropout can Simulate Exponential Number of Models for Sample Selection Techniques RD Samsung 29 0 0 26 Feb 2022
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features Zhaowei Zhu Jialu Wang Yang Liu NoLa 26 37 0 02 Feb 2022
Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification Chunren Tang Dingyu Xue Dongyue Chen 30 2 0 25 Jan 2022
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation Jeongeun Park Seungyoung Shin Sangheum Hwang Sungjoon Choi 15 5 0 02 Nov 2021
Mitigating Memorization of Noisy Labels via Regularization between Representations Hao Cheng Zhaowei Zhu Xing Sun Yang Liu NoLa 33 28 0 18 Oct 2021
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations Sheng Liu Kangning Liu Weicheng Zhu Yiqiu Shen C. Fernandez‐Granda NoLa 26 104 0 07 Oct 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
Learning from Noisy Labels via Dynamic Loss Thresholding Hao Yang Youzhi Jin Zi-Hua Li Deng-Bao Wang Lei Miao Xin Geng Min-Ling Zhang NoLa AI4CE 24 6 0 01 Apr 2021
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification Fengxiang Yang Zhun Zhong Zhiming Luo Yuanzheng Cai Yaojin Lin Shaozi Li N. Sebe NoLa 14 111 0 08 Mar 2021
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels Zhaowei Zhu Yiwen Song Yang Liu NoLa 13 91 0 10 Feb 2021
When Optimizing $f$ -divergence is Robust with Label Noise Jiaheng Wei Yang Liu 24 54 0 07 Nov 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 310 497 0 05 Mar 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible Antonin Berthon Bo Han Gang Niu Tongliang Liu Masashi Sugiyama NoLa 21 104 0 11 Jan 2020