Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion

17 June 2021

Papers citing "Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion"

6 / 6 papers shown

Title
Learning with Noisy Labels through Learnable Weighting and Centroid Similarity F. Wani Maria Sofia Bucarelli Fabrizio Silvestri NoLa 29 3 0 16 Mar 2023
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Approach Cuong C. Nguyen Thanh-Toan Do G. Carneiro NoLa 23 0 0 04 Jan 2023
Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss Lefan Zhang Zhang-Hao Tian Wujun Zhou W. Wang NoLa 19 2 0 20 Nov 2022
Rank-based Decomposable Losses in Machine Learning: A Survey Shu Hu Xin Wang Siwei Lyu 26 32 0 18 Jul 2022
SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels Yangdi Lu Wenbo He NoLa 25 39 0 02 May 2022
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 497 0 05 Mar 2020