BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise
Learning

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

28 May 2023

Bo Han

Masashi Sugiyama

Papers citing "BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning"

8 / 8 papers shown

Title
Exploring Video-Based Driver Activity Recognition under Noisy Labels Linjuan Fan Di Wen Kunyu Peng Kailun Yang J. Zhang ... Yufan Chen Junwei Zheng Jiamin Wu Xudong Han Rainer Stiefelhagen NoLa 47 0 0 16 Apr 2025
Accurate Forgetting for Heterogeneous Federated Continual Learning Abudukelimu Wuerkaixi Sen Cui J. Zhang Kunda Yan Bo Han Gang Niu Lei Fang Changshui Zhang Masashi Sugiyama 97 7 0 20 Feb 2025
NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Tao Wu Chuhao Zhou Yen Heng Wong Lin Gu Jianfei Yang 74 1 0 14 Dec 2024
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels Yuxin Tian Mouxing Yang Yuhao Zhou Jian Wang Qing Ye Tongliang Liu Gang Niu Jiancheng Lv FedML 53 0 0 30 Nov 2024
An Embedding is Worth a Thousand Noisy Labels Francesco Di Salvo Sebastian Doerrich Ines Rieger Christian Ledig NoLa 56 0 0 26 Aug 2024
Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs Shuqi He Jun Zhuang Ding Wang Luyao Peng Jun Song AAML 26 1 0 05 Jun 2024
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 22 55 0 10 Dec 2020
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 244 35,884 0 25 Aug 2016