Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

29 November 2021

Papers citing "Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection"

6 / 6 papers shown

Title
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
Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification Xiaoyang Hao Zhixi Feng Tongqing Peng Shuyuan Yang NoLa 28 5 0 09 Aug 2024
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 112 120 0 04 Feb 2021
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 92 34 0 08 Dec 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017
Learning from Binary Labels with Instance-Dependent Corruption A. Menon Brendan van Rooyen Nagarajan Natarajan NoLa 31 41 0 03 May 2016