SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

2 December 2020

Bo Han

Papers citing "SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning"

7 / 7 papers shown

Title
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation Lars Schmarje Vasco Grossmann Claudius Zelenka S. Dippel R. Kiko ... M. Pastell J. Stracke A. Valros N. Volkmann Reinahrd Koch 37 34 0 13 Jul 2022
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels Zhuowei Wang Tianyi Zhou Guodong Long Bo Han Jing Jiang FedML 27 19 0 20 May 2022
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling Bowen Zhang Yidong Wang Wenxin Hou Hao Wu Jindong Wang Manabu Okumura T. Shinozaki AAML 226 862 0 15 Oct 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Xian-Jin Gui Wei Wang Zhang-Hao Tian NoLa 25 44 0 17 Jun 2021
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 310 497 0 05 Mar 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 317 11,681 0 09 Mar 2017
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 261 1,275 0 06 Mar 2017