Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration

11 October 2022

Papers citing "Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration"

8 / 8 papers shown

Title
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation Lexin Fang Yunyang Xu Xiang Ma Xuemei Li Caiming Zhang 46 0 0 14 Mar 2025
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method Manyi Zhang Xuyang Zhao Jun Yao Chun Yuan Weiran Huang 17 19 0 20 Nov 2022
Co-learning: Learning from Noisy Labels with Self-supervision Cheng Tan Jun-Xiong Xia Lirong Wu Stan Z. Li NoLa 68 116 0 05 Aug 2021
Correlated Input-Dependent Label Noise in Large-Scale Image Classification Mark Collier Basil Mustafa Efi Kokiopoulou Rodolphe Jenatton Jesse Berent NoLa 176 53 0 19 May 2021
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
Free Lunch for Few-shot Learning: Distribution Calibration Shuo Yang Lu Liu Min Xu OODD 208 322 0 16 Jan 2021
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 494 0 05 Mar 2020
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 47 172 0 24 May 2019