Investigating Why Contrastive Learning Benefits Robustness Against Label
Noise

Investigating Why Contrastive Learning Benefits Robustness Against Label Noise

29 January 2022

Kyle Whitecross

Baharan Mirzasoleiman

Papers citing "Investigating Why Contrastive Learning Benefits Robustness Against Label Noise"

10 / 10 papers shown

Title
Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data Weiran Pan Wei Wei Feida Zhu Yong Deng NoLa 203 0 0 24 Apr 2025
IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification Zhixun Li Dingshuo Chen Tong Zhao Ziyu Zhao Hongrui Liu Qing Cui Jun Zhou Jeffrey Xu Yu SSL 105 0 0 10 Feb 2025
Multi-level Supervised Contrastive Learning Naghmeh Ghanooni Barbod Pajoum Harshit Rawal Sophie Fellenz Vo Nguyen Le Duy Marius Kloft 86 0 0 04 Feb 2025
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Jingyi Cui Yi-Ge Zhang Hengyu Liu Yisen Wang NoLa 48 0 0 03 Jan 2025
Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency Training Huayi Zhou Mukun Luo Fei Jiang Yue Ding Hongtao Lu Kui Jia 46 0 0 18 Feb 2024
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets? Bidur Khanal Binod Bhattarai Bishesh Khanal Cristian A. Linte NoLa 21 0 0 15 Jan 2024
Cross-domain Transfer of defect features in technical domains based on partial target data T. Schlagenhauf Tim Scheurenbrand 36 1 0 24 Nov 2022
Robust Training for Speaker Verification against Noisy Labels Zhihua Fang Liang He Hanhan Ma Xiao-Min Guo Lin Li NoLa 24 3 0 22 Nov 2022
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method Manyi Zhang Xuyang Zhao Jun Yao Chun Yuan Weiran Huang 44 20 0 20 Nov 2022
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection Umar Khalid Ashkan Esmaeili Nazmul Karim Nazanin Rahnavard OODD 45 12 0 06 Apr 2022