Dimensionality-Driven Learning with Noisy Labels

7 June 2018

Papers citing "Dimensionality-Driven Learning with Noisy Labels"

50 / 78 papers shown

Title
Sample Selection via Contrastive Fragmentation for Noisy Label Regression C. Kim Sangwoo Moon Jihwan Moon Dongyeon Woo Gunhee Kim NoLa 54 0 0 25 Feb 2025
Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data Chao Liang Linchao Zhu Zongxin Yang Wei Chen Yi Yang NoLa 59 0 0 05 Jan 2025
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Jingyi Cui Yi-Ge Zhang Hengyu Liu Yisen Wang NoLa 43 0 0 03 Jan 2025
Effective and Robust Adversarial Training against Data and Label Corruptions Pengfei Zhang Zi Huang Xin-Shun Xu Guangdong Bai 43 4 0 07 May 2024
LDReg: Local Dimensionality Regularized Self-Supervised Learning Hanxun Huang R. Campello S. Erfani Xingjun Ma Michael E. Houle James Bailey 28 5 0 19 Jan 2024
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis Alastair Anderberg James Bailey R. Campello Michael E. Houle Henrique O. Marques Milovs Radovanović Arthur Zimek 31 0 0 10 Jan 2024
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels Jichang Li Guanbin Li Hui Cheng Zicheng Liao Yizhou Yu FedML 29 14 0 19 Dec 2023
Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels Yongquan Yang Hong Bu 21 0 0 06 Jul 2023
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization Ramnath Kumar Kushal Majmundar Dheeraj M. Nagaraj A. Suggala ODL 24 6 0 15 Jun 2023
Similarity of Neural Network Models: A Survey of Functional and Representational Measures Max Klabunde Tobias Schumacher M. Strohmaier Florian Lemmerich 52 64 0 10 May 2023
Can we avoid Double Descent in Deep Neural Networks? Victor Quétu Enzo Tartaglione AI4CE 20 3 0 26 Feb 2023
FedDebug: Systematic Debugging for Federated Learning Applications Waris Gill A. Anwar Muhammad Ali Gulzar FedML 26 11 0 09 Jan 2023
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels Yikai Wang Yanwei Fu Xinwei Sun NoLa 52 8 0 02 Jan 2023
CrossSplit: Mitigating Label Noise Memorization through Data Splitting Jihye Kim A. Baratin Yan Zhang Simon Lacoste-Julien NoLa 18 7 0 03 Dec 2022
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method Manyi Zhang Xuyang Zhao Jun Yao Chun Yuan Weiran Huang 30 19 0 20 Nov 2022
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels Daehwan Kim Kwang-seok Ryoo Hansang Cho Seung Wook Kim NoLa 24 3 0 20 Nov 2022
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels Daniel Shwartz Uri Stern D. Weinshall NoLa 33 2 0 02 Oct 2022
Class-Imbalanced Complementary-Label Learning via Weighted Loss Meng Wei Yong Zhou Zhongnian Li Xinzheng Xu 13 13 0 28 Sep 2022
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling Boshen Zhang Yuxi Li Yuanpeng Tu Jinlong Peng Yabiao Wang Cunlin Wu Yanghua Xiao Cairong Zhao NoLa 31 6 0 23 Aug 2022
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning D. Hoang Cuong C. Nguyen Cuong Nguyen anh Belagiannis Vasileios G. Carneiro 13 2 0 17 Aug 2022
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels Ganlong Zhao Guanbin Li Yipeng Qin Feng Liu Yizhou Yu NoLa 19 22 0 29 Jul 2022
Robust Object Detection With Inaccurate Bounding Boxes Chengxin Liu Kewei Wang Hao Lu Zhiguo Cao Ziming Zhang 11 21 0 20 Jul 2022
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets Yingsong Huang Bing Bai Shengwei Zhao Kun Bai Fei-Yue Wang NoLa 23 43 0 12 Jul 2022
Sparse Double Descent: Where Network Pruning Aggravates Overfitting Zhengqi He Zeke Xie Quanzhi Zhu Zengchang Qin 69 27 0 17 Jun 2022
The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation Lin Li Long Chen Yifeng Huang Zhimeng Zhang Songyang Zhang Jun Xiao NoLa 26 72 0 07 Jun 2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation De-Chun Cheng Tongliang Liu Yixiong Ning Nannan Wang Bo Han Gang Niu Xinbo Gao Masashi Sugiyama NoLa 37 65 0 06 Jun 2022
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning Nazmul Karim Mamshad Nayeem Rizve Nazanin Rahnavard Ajmal Saeed Mian M. Shah NoLa 30 97 0 28 Mar 2022
Selective-Supervised Contrastive Learning with Noisy Labels Shikun Li Xiaobo Xia Shiming Ge Tongliang Liu NoLa 19 172 0 08 Mar 2022
Dropout can Simulate Exponential Number of Models for Sample Selection Techniques RD Samsung 27 0 0 26 Feb 2022
Do We Need to Penalize Variance of Losses for Learning with Label Noise? Yexiong Lin Yu Yao Yuxuan Du Jun Yu Bo Han Mingming Gong Tongliang Liu NoLa 41 3 0 30 Jan 2022
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks Tolga Birdal Aaron Lou Leonidas J. Guibas Umut cSimcsekli 16 60 0 25 Nov 2021
Sample Selection for Fair and Robust Training Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh 13 61 0 27 Oct 2021
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression Jiabo He S. Erfani Xingjun Ma James Bailey Ying Chi Xiansheng Hua 28 247 0 26 Oct 2021
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels F. Cordeiro Vasileios Belagiannis Ian Reid G. Carneiro NoLa 22 18 0 22 Oct 2021
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer Yongquan Yang Fengling Li Yani Wei Jie Chen Ning Chen Mohammad H. Alobaidi Hong Bu 18 8 0 20 Oct 2021
Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs David Bonet Antonio Ortega Javier Ruiz-Hidalgo Sarath Shekkizhar GNN 31 7 0 18 Oct 2021
Continual Learning on Noisy Data Streams via Self-Purified Replay C. Kim Jinseo Jeong Sang-chul Moon Gunhee Kim CLL 32 39 0 14 Oct 2021
Local Intrinsic Dimensionality Signals Adversarial Perturbations Sandamal Weerasinghe T. Alpcan S. Erfani C. Leckie Benjamin I. P. Rubinstein AAML 15 0 0 24 Sep 2021
Assessing the Quality of the Datasets by Identifying Mislabeled Samples Vaibhav Pulastya Gaurav Nuti Yash Kumar Atri Tanmoy Chakraborty NoLa 25 5 0 10 Sep 2021
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training A. Gómez-Ríos Julián Luengo Francisco Herrera OOD NoLa 19 0 0 08 Sep 2021
NGC: A Unified Framework for Learning with Open-World Noisy Data Zhi-Fan Wu Tong Wei Jianwen Jiang Chaojie Mao Mingqian Tang Yu-Feng Li 11 80 0 25 Aug 2021
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering Nairouz Mrabah Mohamed Bouguessa M. Touati Riadh Ksantini 28 62 0 19 Jul 2021
Adaptive Sample Selection for Robust Learning under Label Noise Deep Patel P. Sastry OOD NoLa 28 29 0 29 Jun 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Xian-Jin Gui Wei Wang Zhang-Hao Tian NoLa 25 44 0 17 Jun 2021
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs Enyan Dai Charu C. Aggarwal Suhang Wang NoLa 25 114 0 08 Jun 2021
Self-paced Resistance Learning against Overfitting on Noisy Labels Xiaoshuang Shi Zhenhua Guo Fuyong Xing Yun Liang Xiaofeng Zhu NoLa 19 20 0 07 May 2021
Boosting Co-teaching with Compression Regularization for Label Noise Yingyi Chen Xin Shen S. Hu Johan A. K. Suykens NoLa 37 45 0 28 Apr 2021
Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification Youngwoo Kim Cheng Li Bingyang Ye A. Tahmasebi J. Aslam NoLa 17 1 0 13 Mar 2021
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment F. Cordeiro Ragav Sachdeva Vasileios Belagiannis Ian Reid G. Carneiro NoLa 11 77 0 06 Mar 2021
DST: Data Selection and joint Training for Learning with Noisy Labels Yi Wei Xue Mei Xin Liu Pengxiang Xu NoLa 19 3 0 01 Mar 2021