Asymmetric Loss Functions for Learning with Noisy Labels

Asymmetric Loss Functions for Learning with Noisy Labels

6 June 2021

Xin Gao

Papers citing "Asymmetric Loss Functions for Learning with Noisy Labels"

7 / 7 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 164 0 0 24 Apr 2025
An Embedding is Worth a Thousand Noisy Labels Francesco Di Salvo Sebastian Doerrich Ines Rieger Christian Ledig NoLa 73 0 0 26 Aug 2024
SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels Han Yang Tianyu Wang Xiao Hu Chi-Wing Fu NoLa 51 13 0 23 Aug 2023
Mitigating Label Noise through Data Ambiguation Julian Lienen Eyke Hüllermeier NoLa 32 6 0 23 May 2023
Latent Class-Conditional Noise Model Jiangchao Yao Bo Han Zhihan Zhou Ya-Qin Zhang Ivor W. Tsang NoLa BDL 33 8 0 19 Feb 2023
Learning with Noisy Labels via Sparse Regularization Xiong Zhou Xianming Liu Chenyang Wang Deming Zhai Junjun Jiang Xiangyang Ji NoLa 26 51 0 31 Jul 2021
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 61 172 0 24 May 2019