Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension

6 May 2024

Papers citing "Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension"

9 / 9 papers shown

Title
Early Stopping Against Label Noise Without Validation Data Suqin Yuan Lei Feng Tongliang Liu NoLa 169 18 0 11 Feb 2025
Multi-annotator Deep Learning: A Probabilistic Framework for Classification M. Herde Denis Huseljic Bernhard Sick 53 9 0 05 Apr 2023
A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification M. Herde Denis Huseljic Bernhard Sick A. Calma 48 25 0 23 Sep 2021
End-to-End Weak Supervision Salva Rühling Cachay Benedikt Boecking A. Dubrawski NoLa 49 41 0 05 Jul 2021
Learning from Crowds by Modeling Common Confusions Zhendong Chu Jing Ma Hongning Wang NoLa 14 47 0 24 Dec 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 82 979 0 16 Jul 2020
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey G. Algan ilkay Ulusoy NoLa VLM 53 327 0 11 Dec 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 572 4,735 0 13 May 2019
SGDR: Stochastic Gradient Descent with Warm Restarts I. Loshchilov Frank Hutter ODL 227 8,030 0 13 Aug 2016