A curated list of papers that study learning with noisy labels.
- [2014 TNNLS] Classification in the Presence of Label Noise: A Survey [paper]
- [2019 KBS] Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey [paper]
- [2020 MIA] Deep learning with noisy labels: exploring techniques and remedies in medical image analysis [paper]
- [2020 ArXiv] A Survey of Label-noise Representation Learning: Past, Present and Future [paper] [code]
- [2022 TNNLS] Learning from Noisy Labels with Deep Neural Networks: A Survey [paper] [code]
- Tongliang Liu, The University of Sydney
- Bo Han, Hong Kong Baptist University
- Yang Liu, UC Santa Cruz
- RIKEN-AIP, Japan
- [ArXiv] DN-TOD: Haoran Zhua, Chang Xua, Wen Yanga, Ruixiang Zhanga, Yan Zhanga, Gui-Song Xia.
"Robust Tiny Object Detection in Aerial Images amidst Label Noise." [paper]
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[ICCV 2023] SSD-Det: Di Wu, Pengfei Chen, Xuehui Yu, Guorong Li, Zhenjun Han, Jianbin Jiao.
"Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes." [paper] [code] -
[ArXiv 2023] Donghao Zhou, Jialin Li, Jinpeng Li, Jiancheng Huang, Qiang Nie, Yong Liu, Bin-Bin Gao, Qiong Wang, Pheng-Ann Heng, Guangyong Chen.
"Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes." [paper] -
[ArXiv 2023] Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Siniša Šegvic, Matthias Rottmann.
"Identifying Label Errors in Object Detection Datasets by Loss Inspection." [paper] -
[ArXiv 2023] UNA: Kwangrok Ryoo, Yeonsik Jo, Seungjun Lee, Mira Kim, Ahra Jo, Seung Hwan Kim, Seungryong Kim, Soonyoung Lee.
"Universal Noise Annotation: Unveiling the Impact of Noisy Annotation on Object Detection." [paper] [code]
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[CVPR 2022] NLTE: Xinyu Liu, Wuyang Li, Qiushi Yang, Baopu Li, Yixuan Yuan.
"Towards Robust Adaptive Object Detection under Noisy Annotations." [paper] [code] -
[ECCV 2022] OA-MIL: Chengxin Liu, Kewei Wang, Hao Lu, Zhiguo Cao, Ziming Zhang.
"Robust Object Detection With Inaccurate Bounding Boxes." [paper] [code] -
[ECCV 2022] W2N: Zitong Huang, Yiping Bao, Bowen Dong, Erjin Zhou, Wangmeng Zuo.
"W2N: Switching From Weak Supervision to Noisy Supervision for Object Detection." [paper] [code] -
[Remote Sensing 2022] Maximilian Bernhard, Matthias Schubert.
"Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications." [paper] -
[TIP 2022] Shaoru Wang, Jin Gao, Bing Li, Weiming Hu.
"Narrowing the Gap: Improved Detector Training with Noisy Location Annotations." [paper] -
[ArXiv 2022] Krystian Chachuła, Adam Popowicz, Jakub Łyskawa, Bartłomiej Olber, Piotr Fr ̨ atczak, Krystian Radlak.
"Combating noisy labels in object detection datasets." [paper] -
[ACM GIS 2022] Maximilian Bernhard, Matthias Schubert.
"Robust object detection in remote sensing imagery with noisy and sparse geo-annotations."
[paper] [code]
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[TIP 2021] MRNet: Youjiang Xu, Linchao Zhu, YiYang, Fei Wu.
"Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes." [paper] -
[BMVC 2021] Jiafeng Mao, Qing Yu, Yoko Yamakata, Kiyoharu Aizawa.
"Noisy Annotation Refinement for Object Detection" [paper] -
[IEICE TIS 2021] Jiafeng Mao, Qing Yu, Kiyoharu Aizawa.
"Noisy Localization Annotation Refinement for Object Detection." [paper]
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[CVPR 2020] Yunhang Shen, Rongrong Ji, Zhiwei Chen, Xiaopeng Hong, Feng Zheng, Jianzhuang Liu, Mingliang Xu, Qi Tian.
"Noise-Aware Fully Webly Supervised Object Detection." [paper] [code] -
[CVPR 2020] Hengduo Li, Zuxuan Wu, Chen Zhu, Caiming Xiong, Richard Socher, Larry S. Davis.
"Learning From Noisy Anchors for One-Stage Object Detection." [paper] -
[CVPRW 2020] Aybora Koksal, Kutalmis Gokalp Ince, A. Aydin Alatan.
"Effect of Annotation Errors on Drone Detection with YOLOv3." [paper] -
[ICIP 2020] Jiafeng Mao, Qing Yu, Kiyoharu Aizawa.
"Noisy Localization Annotation Refinement For Object Detection." [paper] -
[ArXiv 2020] Junnan Li, Caiming Xiong, Richard Socher, Steven Hoi.
"Towards Noise-resistant Object Detection with Noisy Annotations." [paper]
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[ICCV 2019] NOTE-RCNN: Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia.
"NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection." [paper] -
[AAAI 2019] SD-LocNet: Xiaopeng Zhang, Yang Yang, Jiashi Feng.
"Learning to Localize Objects with Noisy Labeled Instances." [paper] -
[IV 2019] Simon Chadwick, Paul Newman.
"Training object detectors with noisy data." [paper]
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[ICLR 2023] Jiachen Yao, Yikai Zhang, Songzhu Zheng, Mayank Goswami, Prateek Prasanna, Chao Chen.
"Learning to Segment From Noisy Annotations: A Spatial Correction Approach." [paper] [code] -
[ArXiv 2023] Zicheng Wang, Zhen Zhao, Erjian Guo, Luping Zhou.
"Clean Label Disentangling for Medical Image Segmentation with Noisy Labels." [paper] [code]
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[CVPR 2022 oral] Sheng Liu, Kangning Liu, Weicheng Zhu, Yiqiu Shen, Carlos Fernandez-Granda.
"Adaptive Early-Learning Correction for Segmentation from Noisy Annotations." [paper] [code] -
[CVPR 2022] SimT: Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan. "SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation." [paper] [code]
- (TPAMI version) Handling Open-set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation. [paper]
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[AAAI 2022] Yaoru Luo, Guole Liu, Yuanhao Guo, Ge Yang.
"Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation." [paper] [code]
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[CVPR 2021] Youngmin Oh, Beomjun Kim, Bumsub Ham.
"Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation." [paper] [code] -
[ICCV 2021 oral] Shuquan Ye, Dongdong Chen, Songfang Han, Jing Liao.
"Learning with Noisy Labels for Robust Point Cloud Segmentation." [paper] [code]- (TPAMI version) Robust Point Cloud Segmentation with Noisy Annotations. [paper]
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[ICCV 2021] Yuxi Wang, Junran Peng, Zhaoxiang Zhang.
"Uncertainty-aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation." [paper] -
[IJCV 2021] Zhedong Zheng, Yi Yang.
"Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation." [paper] [code]- [IJCAI 2020 Conference Version] Unsupervised Scene Adaptation with Memory Regularization in vivo [paper]
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[ECCV 2020] Longrong Yang, Fanman Meng, Hongliang Li, Qingbo Wu, Qishang Cheng.
"Learning with Noisy Class Labels for Instance Segmentation." [paper] [code] -
[NeurIPS 2020] Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga Ciccarelli, Frederik Barkhof, Daniel C. Alexander.
"Disentangling Human Error from the Ground Truth in Segmentation of Medical Images." [paper] [code] -
[MICCAI 2020] Minqing Zhang, Jiantao Gao, Zhen Lyu, Weibing Zhao, Qin Wang, Weizhen Ding, Sheng Wang, Zhen Li, Shuguang Cui.
"Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation." [paper] [code]
- [CVPR 2019] Yi Zhu, Karan Sapra, Fitsum A. Reda, Kevin J. Shih, Shawn Newsam, Andrew Tao, Bryan Catanzaro.
"Improving Semantic Segmentation via Video Propagation and Label Relaxation." [paper] [code]
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[TPAMI 2023] Jia Wan, Qiangqiang Wu, Antoni B. Chan.
"Modeling Noisy Annotations for Point-wise Supervision." [paper] -
[ArXiv 2023] SACC-Net: Yi-Kuan Hsieh, Jun-Wei Hsieh, Xin li, Ming-Ching Chang, Yu-Chee Tseng.
"Scale-Aware Crowd Count Network with Annotation Error Correction." [paper] -
[ArXiv 2023] Yuda Zou, Xin Xiao, Peilin Zhou, Zhichao Sun, Bo Du, Yongchao Xu.
"Noised Autoencoders for Point Annotation Restoration in Object Counting." [paper] -
[ArXiv 2023] Yuehai Chen, Jing Yang, Badong Chen, Shaoyi Du, Gang Hua. "Point Annotation Probability Map: Towards Dense Object Counting by Tolerating Annotation Noise." [paper]
