Robustness and Reliability When Training With Noisy Labels

Robustness and Reliability When Training With Noisy Labels

7 October 2021

Fredrik Lindsten

Papers citing "Robustness and Reliability When Training With Noisy Labels"

7 / 7 papers shown

Title
Robustness and Cybersecurity in the EU Artificial Intelligence Act Henrik Nolte Miriam Rateike Michèle Finck 38 1 0 22 Feb 2025
Synergy-Guided Regional Supervision of Pseudo Labels for Semi-Supervised Medical Image Segmentation Tao Wang Xinlin Zhang Yuanbin Chen Yuanbo Zhou Longxuan Zhao T. Tan Tong Tong 24 0 0 07 Nov 2024
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise? Galadrielle Humblot-Renaux Sergio Escalera T. Moeslund OODD UQCV NoLa 31 5 0 02 Apr 2024
Regularly Truncated M-estimators for Learning with Noisy Labels Xiaobo Xia Pengqian Lu Chen Gong Bo Han Jun-chen Yu Jun Yu Tongliang Liu NoLa 26 9 0 02 Sep 2023
Collective Human Opinions in Semantic Textual Similarity Yuxia Wang Shimin Tao Ning Xie Hao Yang Timothy Baldwin Karin Verspoor 29 4 0 08 Aug 2023
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels Arpit Garg Cuong C. Nguyen Rafael Felix Thanh-Toan Do G. Carneiro 22 2 0 20 Mar 2023
A Survey on Preserving Fairness Guarantees in Changing Environments Ainhize Barrainkua Paula Gordaliza Jose A. Lozano Novi Quadrianto FaML 27 3 0 14 Nov 2022