SELF: Learning to Filter Noisy Labels with Self-Ensembling

4 October 2019

Thi Phuong Nhung Ngo

Papers citing "SELF: Learning to Filter Noisy Labels with Self-Ensembling"

50 / 172 papers shown

Title
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
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy Lars Schmarje Johannes Brunger M. Santarossa Simon-Martin Schroder R. Kiko Reinhard Koch 39 17 0 13 Oct 2021
Consistency Regularization Can Improve Robustness to Label Noise Erik Englesson Hossein Azizpour NoLa 86 20 0 04 Oct 2021
Robust Temporal Ensembling for Learning with Noisy Labels Abel Brown Benedikt D. Schifferer R. DiPietro NoLa OOD 6 0 0 29 Sep 2021
Connecting Low-Loss Subspace for Personalized Federated Learning S. Hahn Minwoo Jeong Junghye Lee FedML 14 18 0 16 Sep 2021
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training Yu Meng Yunyi Zhang Jiaxin Huang Xuan Wang Yu Zhang Heng Ji Jiawei Han 45 69 0 10 Sep 2021
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction Kailong Hao Botao Yu Wei Hu 17 18 0 05 Sep 2021
Robust Long-Tailed Learning under Label Noise Tong Wei Jiang-Xin Shi Wei-Wei Tu Yu-Feng Li NoLa 17 50 0 26 Aug 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
Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering Tapas Nayak Navonil Majumder Soujanya Poria 11 7 0 22 Aug 2021
Confidence Adaptive Regularization for Deep Learning with Noisy Labels Yangdi Lu Yang Bo Wenbo He NoLa 20 10 0 18 Aug 2021
Co-learning: Learning from Noisy Labels with Self-supervision Cheng Tan Jun-Xiong Xia Lirong Wu Stan Z. Li NoLa 68 116 0 05 Aug 2021
Understanding and Improving Early Stopping for Learning with Noisy Labels Ying-Long Bai Erkun Yang Bo Han Yanhua Yang Jiatong Li Yinian Mao Gang Niu Tongliang Liu NoLa 25 206 0 30 Jun 2021
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks Dongha Kim Yongchan Choi Kunwoong Kim Yongdai Kim NoLa 11 0 0 29 Jun 2021
Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image Classification Mengdi Gao Ximeng Feng Mufeng Geng Zhe Jiang Lei Zhu Xiangxi Meng Chuanqing Zhou Qiushi Ren Yanye Lu BDL NoLa 19 6 0 23 Jun 2021
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise Hongxin Wei Lue Tao Renchunzi Xie Bo An NoLa 16 83 0 21 Jun 2021
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels Seulki Park Hwanjun Song Daeho Um D. Jo Sangdoo Yun J. Choi NoLa 19 0 0 14 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
Instance Correction for Learning with Open-set Noisy Labels Xiaobo Xia Tongliang Liu Bo Han Mingming Gong Jun Yu Gang Niu Masashi Sugiyama NoLa 10 12 0 01 Jun 2021
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels Xiaobo Xia Tongliang Liu Bo Han Mingming Gong Jun Yu Gang Niu Masashi Sugiyama NoLa 15 110 0 01 Jun 2021
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network Shuo Yang Erkun Yang Bo Han Yang Liu Min Xu Gang Niu Tongliang Liu NoLa BDL 24 42 0 27 May 2021
Learning Robust Recommenders through Cross-Model Agreement Yu-Xiang Wang Xin Xin Zaiqiao Meng Xiangnan He J. Jose Fuli Feng 13 47 0 20 May 2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels Erik Englesson Hossein Azizpour NoLa 26 103 0 10 May 2021
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise Andrea Castellani Sebastian Schmitt Barbara Hammer NoLa 22 18 0 01 May 2021
A Framework using Contrastive Learning for Classification with Noisy Labels Madalina Ciortan R. Dupuis Thomas Peel VLM NoLa 19 12 0 19 Apr 2021
On Universal Black-Box Domain Adaptation Bin Deng Yabin Zhang Hui Tang Changxing Ding K. Jia 17 9 0 10 Apr 2021
A Theoretical Analysis of Learning with Noisily Labeled Data Yi Tian Xu Qi Qian Hao Li R. L. Jin NoLa 23 0 0 08 Apr 2021
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation Chen Li G. Lee OOD 9 81 0 27 Mar 2021
Transform consistency for learning with noisy labels Rumeng Yi Yaping Huang NoLa 14 4 0 25 Mar 2021
Approximating Instance-Dependent Noise via Instance-Confidence Embedding Yivan Zhang Masashi Sugiyama 31 8 0 25 Mar 2021
Co-matching: Combating Noisy Labels by Augmentation Anchoring Yangdi Lu Yang Bo Wenbo He NoLa 19 7 0 23 Mar 2021
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels Purvi Goel Li Chen NoLa 28 15 0 22 Mar 2021
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning Ragav Sachdeva F. Cordeiro Vasileios Belagiannis Ian Reid G. Carneiro 29 34 0 21 Mar 2021
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation Naveen Venkat Jogendra Nath Kundu D. K. Singh Ambareesh Revanur R. VenkateshBabu 17 69 0 20 Mar 2021
MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels G. Algan ilkay Ulusoy NoLa 11 12 0 19 Mar 2021
Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction Hao-Chiang Shao Hsin-Chieh Wang Weng-Tai Su Chia-Wen Lin NoLa 14 6 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
Multiplicative Reweighting for Robust Neural Network Optimization Noga Bar Tomer Koren Raja Giryes OOD NoLa 11 9 0 24 Feb 2021
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation Weijie Chen Luojun Lin Shicai Yang Di Xie Shiliang Pu Yueting Zhuang Wenqi Ren NoLa SSL 25 57 0 23 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 39 144 0 11 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan S. Kankanhalli Masashi Sugiyama AAML 12 27 0 06 Feb 2021
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization Yivan Zhang Gang Niu Masashi Sugiyama NoLa 28 78 0 04 Feb 2021
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 122 120 0 04 Feb 2021
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning Lang Huang Chaoning Zhang Hongyang R. Zhang SSL 20 24 0 21 Jan 2021
Robust Collaborative Learning with Noisy Labels Mengying Sun Jing Xing B. Chen Jiayu Zhou NoLa 14 3 0 26 Dec 2020
Identifying Training Stop Point with Noisy Labeled Data Sree Ram Kamabattula V. Devarajan Babak Namazi G. Sankaranarayanan NoLa 6 2 0 24 Dec 2020
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels? Jingling Li Mozhi Zhang Keyulu Xu John P. Dickerson Jimmy Ba OOD NoLa 22 19 0 23 Dec 2020
A Second-Order Approach to Learning with Instance-Dependent Label Noise Zhaowei Zhu Tongliang Liu Yang Liu NoLa 12 126 0 22 Dec 2020
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 29 122 0 10 Dec 2020
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting Hongxin Wei Lei Feng R. Wang Bo An NoLa 17 6 0 09 Dec 2020