FINE Samples for Learning with Noisy Labels

FINE Samples for Learning with Noisy Labels

23 February 2021

Papers citing "FINE Samples for Learning with Noisy Labels"

10 / 10 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 100 0 0 24 Apr 2025
Sample Selection via Contrastive Fragmentation for Noisy Label Regression C. Kim Sangwoo Moon Jihwan Moon Dongyeon Woo Gunhee Kim NoLa 52 0 0 25 Feb 2025
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels Chuanyan Hu Shipeng Yan Zhitong Gao Xuming He NoLa 16 4 0 20 Jun 2023
Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning Jiayi Han Longbin Zeng Liang Du Weiyang Ding Jianfeng Feng OOD TTA 19 13 0 15 Jan 2023
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels Sumyeong Ahn Se-Young Yun NoLa 19 2 0 01 Dec 2022
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration Manyi Zhang Yuxin Ren Zihao W. Wang C. Yuan 16 3 0 11 Oct 2022
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss B. Felfeliyan A. Hareendranathan G. Kuntze S. Wichuk Nils D. Forkert Jacob L. Jaremko J. Ronsky NoLa 21 2 0 16 Sep 2022
Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation Yifan Wang Lin Zhang Ran Song Hongliang Li Lin Ma Wei Emma Zhang 17 6 0 19 Jul 2022
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors Jianfei Yang Xiangyu Peng K. Wang Zheng Hua Zhu Jiashi Feng Lihua Xie Yang You 24 27 0 28 May 2022
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