Augmentation Strategies for Learning with Noisy Labels

3 March 2021

Papers citing "Augmentation Strategies for Learning with Noisy Labels"

26 / 26 papers shown

Title
Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization Kuan Zhang Chengliang Chai Jingzhe Xu Chi Zhang Ye Yuan Guoren Wang Lei Cao NoLa 61 0 0 01 May 2025
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 156 0 0 24 Apr 2025
A Language Anchor-Guided Method for Robust Noisy Domain Generalization Zilin Dai Lehong Wang Fangzhou Lin Yidong Wang Zhigang Li Kazunori D Yamada Ziming Zhang Wang Lu 131 0 0 21 Mar 2025
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels Erjian Guo Zicheng Wang Zhen Zhao Luping Zhou NoLa 61 0 0 12 Jan 2025
LaneCorrect: Self-supervised Lane Detection Ming-Jun Nie Xinyue Cai Han Xu Li Zhang SSL 64 4 0 23 Apr 2024
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis Junlin Hou Jilan Xu Rui Feng Hao Chen 23 0 0 08 Apr 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
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 33 1 0 31 May 2023
Mitigating Label Noise through Data Ambiguation Julian Lienen Eyke Hüllermeier NoLa 32 6 0 23 May 2023
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning Jun Shu Xiang Yuan Deyu Meng Zongben Xu 28 4 0 13 May 2023
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 32 9 0 18 Jan 2023
Robust Cross-vendor Mammographic Texture Models Using Augmentation-based Domain Adaptation for Long-term Breast Cancer Risk Andreas D. Lauritzen M. V. von Euler-Chelpin E. Lynge I. Vejborg Mads Nielsen N. Karssemeijer M. Lillholm 16 3 0 27 Dec 2022
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features Qingrui Jia Xuhong Li Lei Yu Jiang Bian Penghao Zhao Shupeng Li Haoyi Xiong Dejing Dou NoLa 24 5 0 19 Dec 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
The Curious Case of Benign Memorization Sotiris Anagnostidis Gregor Bachmann Lorenzo Noci Thomas Hofmann AAML 46 8 0 25 Oct 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
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels E. Santana G. Carneiro F. Cordeiro NoLa 24 6 0 23 Aug 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 27 22 0 29 Jul 2022
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection Gaoang Wang Yibing Zhan Xinchao Wang Min-Gyoo Song K. Nahrstedt 24 11 0 24 Jul 2022
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets Yingsong Huang Bing Bai Shengwei Zhao Kun Bai Fei-Yue Wang NoLa 23 43 0 12 Jul 2022
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning Nazmul Karim Mamshad Nayeem Rizve Nazanin Rahnavard Ajmal Saeed Mian M. Shah NoLa 30 98 0 28 Mar 2022
L2B: Learning to Bootstrap Robust Models for Combating Label Noise Yuyin Zhou Xianhang Li Fengze Liu Qingyue Wei Xuxi Chen Lequan Yu Cihang Xie M. Lungren Lei Xing NoLa 39 3 0 09 Feb 2022
Sparse Fusion for Multimodal Transformers Yi Ding Alex Rich Mason Wang Noah Stier M. Turk P. Sen Tobias Höllerer ViT 27 7 0 23 Nov 2021
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise Chen Feng Georgios Tzimiropoulos Ioannis Patras NoLa 19 18 0 22 Nov 2021
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels F. Cordeiro Vasileios Belagiannis Ian Reid G. Carneiro NoLa 27 18 0 22 Oct 2021
NGC: A Unified Framework for Learning with Open-World Noisy Data Zhi-Fan Wu Tong Wei Jianwen Jiang Chaojie Mao Mingqian Tang Yu-Feng Li 11 80 0 25 Aug 2021