Confidence Scores Make Instance-dependent Label-noise Learning Possible

11 January 2020

Bo Han

Papers citing "Confidence Scores Make Instance-dependent Label-noise Learning Possible"

50 / 61 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 82 0 0 24 Apr 2025
Segmentation with Noisy Labels via Spatially Correlated Distributions Ryu Tadokoro Tsukasa Takagi Shin-ichi Maeda 24 0 0 21 Apr 2025
Training Robust Graph Neural Networks by Modeling Noise Dependencies Yeonjun In Kanghoon Yoon Sukwon Yun Kibum Kim Sungchul Kim Chanyoung Park OOD NoLa 76 0 0 27 Feb 2025
Learning Causal Transition Matrix for Instance-dependent Label Noise Jiahui Li Tai-wei Chang Kun Kuang Ximing Li Long Chen Jun Zhou NoLa CML 121 0 0 18 Dec 2024
Learning with Confidence: Training Better Classifiers from Soft Labels Sjoerd de Vries Dirk Thierens 16 1 0 24 Sep 2024
Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction Po-Hsuan Huang Chia-Ching Lin Chih-Fan Hsu Ming-Ching Chang Wei-Chao Chen NoLa 16 0 0 10 Jul 2024
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification Hongfei Huang Tingting Liang Xixi Sun Zikang Jin Yuyu Yin NoLa 21 1 0 09 Jul 2024
Foster Adaptivity and Balance in Learning with Noisy Labels Mengmeng Sheng Zeren Sun Tao Chen Shuchao Pang Yucheng Wang Yazhou Yao 29 2 0 03 Jul 2024
Don't drop your samples! Coherence-aware training benefits Conditional diffusion Nicolas Dufour Victor Besnier Vicky Kalogeiton David Picard DiffM 49 2 0 30 May 2024
Can We Treat Noisy Labels as Accurate? Yuxiang Zheng Zhongyi Han Yilong Yin Xin Gao Tongliang Liu 25 1 0 21 May 2024
Trusted Multi-view Learning with Label Noise Cai Xu Yilin Zhang Ziyu Guan Wei Zhao NoLa EDL 39 4 0 18 Apr 2024
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints Jean V. Alves Diogo Leitao Sérgio Jesus Marco O. P. Sampaio Javier Liébana Pedro Saleiro Mário A. T. Figueiredo P. Bizarro 30 5 0 11 Mar 2024
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning Heesun Bae Seungjae Shin Byeonghu Na Il-Chul Moon NoLa 31 3 0 05 Mar 2024
Mitigating Label Noise on Graph via Topological Sample Selection Yuhao Wu Jiangchao Yao Xiaobo Xia Jun-chen Yu Ruxing Wang Bo Han Tongliang Liu NoLa 42 2 0 04 Mar 2024
Label-Noise Robust Diffusion Models Byeonghu Na Yeongmin Kim Heesun Bae Jung Hyun Lee Seho Kwon Wanmo Kang Il-Chul Moon NoLa DiffM 50 8 0 27 Feb 2024
FiFAR: A Fraud Detection Dataset for Learning to Defer Jean V. Alves Diogo Leitao Sérgio Jesus Marco O. P. Sampaio Pedro Saleiro Mário A. T. Figueiredo P. Bizarro 17 0 0 20 Dec 2023
VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models Daniele De Sensi Mingda Zhang Salvatore Di Girolamo Bing Wu Torsten Hoefler MLLM 14 2 0 28 Sep 2023
Unified Risk Analysis for Weakly Supervised Learning Chao-Kai Chiang Masashi Sugiyama 17 3 0 15 Sep 2023
Generating the Ground Truth: Synthetic Data for Label Noise Research Sjoerd de Vries Dirk Thierens NoLa 11 0 0 08 Sep 2023
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise Donna Tjandra Jenna Wiens NoLa 14 3 0 10 Jul 2023
Binary Classification with Instance and Label Dependent Label Noise H. Im Paul Grigas NoLa 14 2 0 06 Jun 2023
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels Jian Chen Ruiyi Zhang Tong Yu Rohan Sharma Zhiqiang Xu Tong Sun Changyou Chen DiffM 25 17 0 31 May 2023
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling Yuchen Zhuang Yue Yu Lingkai Kong Xiang Chen Chao Zhang NoLa SyDa AI4CE 14 13 0 30 May 2023
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals Amila Silva Ling Luo S. Karunasekera C. Leckie 12 5 0 18 May 2023
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning Hanwen Deng Weijia Zhang Min-Ling Zhang NoLa 22 0 0 10 May 2023
Multi-annotator Deep Learning: A Probabilistic Framework for Classification M. Herde Denis Huseljic Bernhard Sick 17 9 0 05 Apr 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
Instance-specific Label Distribution Regularization for Learning with Label Noise Zehui Liao Shishuai Hu Yutong Xie Yong-quan Xia NoLa 11 1 0 16 Dec 2022
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy Reihaneh Torkzadehmahani Reza Nasirigerdeh Daniel Rueckert Georgios Kaissis NoLa 29 7 0 11 Oct 2022
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration Manyi Zhang Yuxin Ren Zihao W. Wang C. Yuan 11 3 0 11 Oct 2022
Instance-Dependent Noisy Label Learning via Graphical Modelling Arpit Garg Cuong C. Nguyen Rafael Felix Thanh-Toan Do G. Carneiro NoLa 21 27 0 02 Sep 2022
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning Trenton Chang Michael Sjoding Jenna Wiens 9 11 0 01 Aug 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 13 6 0 30 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 26 65 0 06 Jun 2022
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model Heesun Bae Seung-Jae Shin Byeonghu Na Joonho Jang Kyungwoo Song Il-Chul Moon NoLa 20 26 0 02 May 2022
Backdoor Defense via Decoupling the Training Process Kunzhe Huang Yiming Li Baoyuan Wu Zhan Qin Kui Ren AAML FedML 11 184 0 05 Feb 2022
Identifiability of Label Noise Transition Matrix Yang Liu Hao Cheng Kun Zhang NoLa 25 42 0 04 Feb 2022
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift Hongrui Liu Binbin Hu Xiao Wang Chuan Shi Zhiqiang Zhang Jun Zhou 90 54 0 27 Jan 2022
Learning with Proper Partial Labels Zheng Wu Jiaqi Lv Masashi Sugiyama 9 8 0 23 Dec 2021
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise Mingcai Chen Hao Cheng Yuntao Du Ming Xu Wenyu Jiang Chongjun Wang NoLa 8 25 0 06 Dec 2021
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection Seong Min Kye Kwanghee Choi Joonyoung Yi Buru Chang NoLa 15 15 0 29 Nov 2021
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation Jeongeun Park Seungyoung Shin Sangheum Hwang Sungjoon Choi 15 5 0 02 Nov 2021
Deep Classifiers with Label Noise Modeling and Distance Awareness Vincent Fortuin Mark Collier F. Wenzel J. Allingham J. Liu Dustin Tran Balaji Lakshminarayanan Jesse Berent Rodolphe Jenatton E. Kokiopoulou UQCV 21 11 0 06 Oct 2021
Instance-dependent Label-noise Learning under a Structural Causal Model Yu Yao Tongliang Liu Mingming Gong Bo Han Gang Niu Kun Zhang CML NoLa 12 68 0 07 Sep 2021
An Instance-Dependent Simulation Framework for Learning with Label Noise Keren Gu Xander Masotto Vandana Bachani Balaji Lakshminarayanan Jack Nikodem Dong Yin NoLa 6 24 0 23 Jul 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei Hangyu Liu Tongliang Liu Gang Niu Masashi Sugiyama Yang Liu NoLa 17 69 0 08 Jun 2021
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network Shuo Yang Erkun Yang Bo Han Yang Liu Min Xu Gang Niu Tongliang Liu NoLa BDL 21 42 0 27 May 2021
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels Zhaowei Zhu Yiwen Song Yang Liu NoLa 6 90 0 10 Feb 2021
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization Yivan Zhang Gang Niu Masashi Sugiyama NoLa 22 77 0 04 Feb 2021
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 112 119 0 04 Feb 2021