Is your noise correction noisy? PLS: Robustness to label noise with two stage detection

10 October 2022

Papers citing "Is your noise correction noisy? PLS: Robustness to label noise with two stage detection"

8 / 8 papers shown

Title
Open set label noise learning with robust sample selection and margin-guided module Yuandi Zhao Qianxi Xia Yang Sun Zhijie Wen Liyan Ma Shihui Ying NoLa 44 0 0 08 Jan 2025
Label Noise: Ignorance Is Bliss Yilun Zhu Jianxin Zhang Aditya Gangrade Clayton Scott NoLa 26 0 0 31 Oct 2024
One-step Noisy Label Mitigation Hao Li Jiayang Gu Jingkuan Song An Zhang Lianli Gao NoLa 24 0 0 02 Oct 2024
An accurate detection is not all you need to combat label noise in web-noisy datasets Paul Albert Jack Valmadre Eric Arazo Tarun Krishna Noel E. O'Connor Kevin McGuinness AAML 36 0 0 08 Jul 2024
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification Zehui Liao Shishuai Hu Yong-quan Xia 33 0 0 18 Jun 2024
Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels Ke Wang Guillermo Ortiz-Jimenez Rodolphe Jenatton Mark Collier Efi Kokiopoulou Pascal Frossard AAML 24 2 0 10 Oct 2023
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise Fahimeh Fooladgar Minh Nguyen Nhat To P. Mousavi Purang Abolmaesumi NoLa 24 4 0 13 Aug 2023
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 218 861 0 15 Oct 2021