Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

14 February 2018

Papers citing "Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise"

50 / 285 papers shown

Title
kNet: A Deep kNN Network To Handle Label Noise Itzik Mizrahi S. Avidan NoLa 21 0 0 20 Jul 2021
Mitigating Memorization in Sample Selection for Learning with Noisy Labels Kyeongbo Kong Junggi Lee Youngchul Kwak Young-Rae Cho Seong-Eun Kim Woo‐Jin Song NoLa 18 0 0 08 Jul 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 36 6 0 23 Jun 2021
Corruption Robust Active Learning Yifang Chen S. Du Kevin G. Jamieson 21 5 0 21 Jun 2021
Distilling effective supervision for robust medical image segmentation with noisy labels Jialin Shi Ji Wu NoLa 19 32 0 21 Jun 2021
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise Hongxin Wei Lue Tao Renchunzi Xie Bo An NoLa 27 83 0 21 Jun 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion Xian-Jin Gui Wei Wang Zhang-Hao Tian NoLa 33 44 0 17 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 32 0 0 14 Jun 2021
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification Kaize Ding Jianling Wang Jundong Li James Caverlee Huan Liu OOD OffRL 26 6 0 12 Jun 2021
Sum of Ranked Range Loss for Supervised Learning Shu Hu Yiming Ying Xin Wang Siwei Lyu 29 23 0 07 Jun 2021
Instance Correction for Learning with Open-set Noisy Labels Xiaobo Xia Tongliang Liu Bo Han Biwei Huang Jun Yu Gang Niu Masashi Sugiyama NoLa 12 12 0 01 Jun 2021
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels Xiaobo Xia Tongliang Liu Bo Han Biwei Huang Jun Yu Gang Niu Masashi Sugiyama NoLa 17 110 0 01 Jun 2021
Training Classifiers that are Universally Robust to All Label Noise Levels Jingyi Xu Tony Q.S. Quek Kai Fong Ernest Chong NoLa 19 2 0 27 May 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 33 42 0 27 May 2021
Faster Meta Update Strategy for Noise-Robust Deep Learning Youjiang Xu Linchao Zhu Lu Jiang Yi Yang 33 51 0 30 Apr 2021
Breast Mass Detection with Faster R-CNN: On the Feasibility of Learning from Noisy Annotations Sina Famouri Lia Morra Leonardo Mangia Fabrizio Lamberti 18 6 0 25 Apr 2021
A Framework using Contrastive Learning for Classification with Noisy Labels Madalina Ciortan R. Dupuis Thomas Peel VLM NoLa 21 12 0 19 Apr 2021
Do We Really Need Gold Samples for Sample Weighting Under Label Noise? Aritra Ghosh Andrew S. Lan NoLa 21 9 0 19 Apr 2021
Joint Negative and Positive Learning for Noisy Labels Youngdong Kim Juseung Yun Hyounguk Shon Junmo Kim NoLa 28 60 0 14 Apr 2021
Self-Training with Weak Supervision Giannis Karamanolakis Subhabrata Mukherjee Guoqing Zheng Ahmed Hassan Awadallah NoLa 26 86 0 12 Apr 2021
On Universal Black-Box Domain Adaptation Bin Deng Yabin Zhang Hui Tang Changxing Ding Kui Jia 25 9 0 10 Apr 2021
Learning from Noisy Labels via Dynamic Loss Thresholding Hao Yang Youzhi Jin Zi-Hua Li Deng-Bao Wang Lei Miao Xin Geng Min-Ling Zhang NoLa AI4CE 32 6 0 01 Apr 2021
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning Shuiyang Mao P. Ching Tan Lee 13 2 0 30 Mar 2021
Robust Audio-Visual Instance Discrimination Pedro Morgado Ishan Misra Nuno Vasconcelos SSL 22 110 0 29 Mar 2021
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks Curtis G. Northcutt Anish Athalye Jonas W. Mueller 30 519 0 26 Mar 2021
Jo-SRC: A Contrastive Approach for Combating Noisy Labels Yazhou Yao Zeren Sun Chuanyi Zhang Fumin Shen Qi Wu Jian Zhang Zhenmin Tang NoLa 33 133 0 24 Mar 2021
Co-matching: Combating Noisy Labels by Augmentation Anchoring Yangdi Lu Yang Bo Wenbo He NoLa 27 7 0 23 Mar 2021
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels Purvi Goel Li Chen NoLa 36 15 0 22 Mar 2021
Enhancing Robustness of On-line Learning Models on Highly Noisy Data Zilong Zhao Robert Birke Rui Han Bogdan Robu S. Bouchenak Sonia Ben Mokhtar L. Chen AAML 16 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 27 6 0 13 Mar 2021
DST: Data Selection and joint Training for Learning with Noisy Labels Yi Wei Xue Mei Xin Liu Pengxiang Xu NoLa 27 3 0 01 Mar 2021
Multiplicative Reweighting for Robust Neural Network Optimization Noga Bar Tomer Koren Raja Giryes OOD NoLa 18 9 0 24 Feb 2021
FINE Samples for Learning with Noisy Labels Taehyeon Kim Jongwoo Ko Sangwook Cho J. Choi Se-Young Yun NoLa 38 103 0 23 Feb 2021
Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction Ayaan Haque V. Reddi Tyler Giallanza NoLa 23 60 0 18 Feb 2021
Fairness-Aware PAC Learning from Corrupted Data Nikola Konstantinov Christoph H. Lampert 11 17 0 11 Feb 2021
Custom Object Detection via Multi-Camera Self-Supervised Learning Yan Lu Yuanchao Shu 19 3 0 05 Feb 2021
Analysing the Noise Model Error for Realistic Noisy Label Data Michael A. Hedderich D. Zhu Dietrich Klakow NoLa 31 19 0 24 Jan 2021
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 30 19 0 23 Dec 2020
From Weakly Supervised Learning to Biquality Learning: an Introduction Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols A. Ouorou 19 21 0 16 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 40 123 0 10 Dec 2020
A Topological Filter for Learning with Label Noise Pengxiang Wu Songzhu Zheng Mayank Goswami Dimitris N. Metaxas Chao Chen NoLa 30 112 0 09 Dec 2020
Multi-Objective Interpolation Training for Robustness to Label Noise Diego Ortego Eric Arazo Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 30 112 0 08 Dec 2020
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 103 34 0 08 Dec 2020
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations? F. Cordeiro G. Carneiro NoLa 49 45 0 05 Dec 2020
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels Xiaobo Xia Tongliang Liu Bo Han Nannan Wang Jiankang Deng Jiatong Li Yinian Mao NoLa 24 11 0 02 Dec 2020
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning Zhuowei Wang Jing Jiang Bo Han Lei Feng Bo An Gang Niu Guodong Long NoLa 33 17 0 02 Dec 2020
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models Taraneh Younesian Chi Hong Amirmasoud Ghiassi Robert Birke L. Chen NoLa FedML 13 3 0 13 Nov 2020
A Survey of Label-noise Representation Learning: Past, Present and Future Bo Han Quanming Yao Tongliang Liu Gang Niu Ivor W. Tsang James T. Kwok Masashi Sugiyama NoLa 24 159 0 09 Nov 2020
Active Learning for Noisy Data Streams Using Weak and Strong Labelers Taraneh Younesian Dick H. J. Epema L. Chen 23 11 0 27 Oct 2020
Importance Reweighting for Biquality Learning Pierre Nodet V. Lemaire A. Bondu Antoine Cornuéjols NoLa 27 6 0 19 Oct 2020