Normalized Loss Functions for Deep Learning with Noisy Labels

24 June 2020

Papers citing "Normalized Loss Functions for Deep Learning with Noisy Labels"

50 / 220 papers shown

Title
Semi-supervised Learning using Robust Loss Wenhui Cui H. Akrami Anand A. Joshi Richard M. Leahy OOD 64 4 0 03 Mar 2022
Robust Training under Label Noise by Over-parameterization Sheng Liu Zhihui Zhu Qing Qu Chong You NoLa OOD 137 114 0 28 Feb 2022
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation Luca Sestini Benoit Rosa Elena De Momi G. Ferrigno N. Padoy 73 35 0 16 Feb 2022
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning Jun Shu Xiang Yuan Deyu Meng Zongben Xu NoLa 147 38 0 11 Feb 2022
Backdoor Defense via Decoupling the Training Process Kunzhe Huang Yiming Li Baoyuan Wu Zhan Qin Kui Ren AAML FedML 83 209 0 05 Feb 2022
PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels A. Goel Yunlong Jiao Jordan Massiah NoLa 90 9 0 26 Jan 2022
Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity Dixian Zhu Yiming Ying Tianbao Yang 184 12 0 30 Dec 2021
One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples Yongquan Yang 59 0 0 08 Dec 2021
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection Seong Min Kye Kwanghee Choi Joonyoung Yi Buru Chang NoLa 138 22 0 29 Nov 2021
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise Chen Feng Georgios Tzimiropoulos Ioannis Patras NoLa 114 20 0 22 Nov 2021
Constrained Instance and Class Reweighting for Robust Learning under Label Noise Abhishek Kumar Ehsan Amid NoLa 96 20 0 09 Nov 2021
Improved Regularization and Robustness for Fine-tuning in Neural Networks Dongyue Li Hongyang R. Zhang NoLa 166 57 0 08 Nov 2021
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation Jeongeun Park Seungyoung Shin Sangheum Hwang Sungjoon Choi 111 6 0 02 Nov 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 165 273 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 89 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 273 9 0 20 Oct 2021
Consistency Regularization Can Improve Robustness to Label Noise Erik Englesson Hossein Azizpour NoLa 462 21 0 04 Oct 2021
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis Wei-wei Zhu Zihe Zheng Haitian Zheng Hanjia Lyu Jiebo Luo NoLa 88 1 0 15 Sep 2021
Confidence Adaptive Regularization for Deep Learning with Noisy Labels Yangdi Lu Yang Bo Wenbo He NoLa 93 10 0 18 Aug 2021
Co-learning: Learning from Noisy Labels with Self-supervision Cheng Tan Jun Xia Lirong Wu Stan Z. Li NoLa 186 136 0 05 Aug 2021
Learning with Noisy Labels via Sparse Regularization Xiong Zhou Xianming Liu Chenyang Wang Deming Zhai Junjun Jiang Xiangyang Ji NoLa 133 59 0 31 Jul 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 130 239 0 30 Jun 2021
Adaptive Sample Selection for Robust Learning under Label Noise Deep Patel P. Sastry OOD NoLa 127 36 0 29 Jun 2021
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise Jianguo Huang Lue Tao Renchunzi Xie Bo An NoLa 133 93 0 21 Jun 2021
Being Properly Improper Tyler Sypherd Richard Nock Lalitha Sankar FaML 135 11 0 18 Jun 2021
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions Xuefeng Du Tian Bian Yu Rong Bo Han Tongliang Liu Qifeng Bai Wenbing Huang Yixuan Li Junzhou Huang NoLa 117 17 0 14 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 132 0 0 14 Jun 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei Hangyu Liu Tongliang Liu Gang Niu Masashi Sugiyama Yang Liu NoLa 244 83 0 08 Jun 2021
Asymmetric Loss Functions for Learning with Noisy Labels Xiong Zhou Xianming Liu Junjun Jiang Xin Gao Xiangyang Ji NoLa 100 75 0 06 Jun 2021
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge Ziyun Li Xinshao Wang Diane Hu N. Robertson David Clifton Christoph Meinel Haojin Yang 106 12 0 02 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 92 13 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 119 123 0 01 Jun 2021
Combining resampling and reweighting for faithful stochastic optimization Jing An Lexing Ying 69 1 0 31 May 2021
Using Early-Learning Regularization to Classify Real-World Noisy Data Alessio Galatolo Alfred Nilsson Roderick Karlemstrand Yineng Wang NoLa 64 1 0 27 May 2021
Training Classifiers that are Universally Robust to All Label Noise Levels Jingyi Xu Tony Q.S. Quek Kai Fong Ernest Chong NoLa 72 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 123 45 0 27 May 2021
Joint Text and Label Generation for Spoken Language Understanding Yang Li Ben Athiwaratkun Cicero Nogueira dos Santos Bing Xiang 90 0 0 11 May 2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels Erik Englesson Hossein Azizpour NoLa 201 117 0 10 May 2021
An Exploration into why Output Regularization Mitigates Label Noise N. Shoham Tomer Avidor Nadav Tal-Israel NoLa 43 0 0 26 Apr 2021
A Framework using Contrastive Learning for Classification with Noisy Labels Madalina Ciortan R. Dupuis Thomas Peel VLM NoLa 67 12 0 19 Apr 2021
Do We Really Need Gold Samples for Sample Weighting Under Label Noise? Aritra Ghosh Andrew Lan NoLa 110 11 0 19 Apr 2021
Contrastive Learning Improves Model Robustness Under Label Noise Aritra Ghosh Andrew Lan NoLa 99 60 0 19 Apr 2021
Joint Negative and Positive Learning for Noisy Labels Youngdong Kim Juseung Yun Hyounguk Shon Junmo Kim NoLa 93 69 0 14 Apr 2021
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond Negin Majidi Ehsan Amid Hossein Talebi Manfred K. Warmuth NoLa 84 19 0 03 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 111 10 0 01 Apr 2021
Approximating Instance-Dependent Noise via Instance-Confidence Embedding Yivan Zhang Masashi Sugiyama 87 8 0 25 Mar 2021
Detecting Label Noise via Leave-One-Out Cross-Validation Yu-Hang Tang Yuanran Zhu W. A. Jong 86 3 0 21 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 118 90 0 06 Mar 2021
Searching for Robustness: Loss Learning for Noisy Classification Tasks Boyan Gao Henry Gouk Timothy M. Hospedales OOD NoLa 118 20 0 27 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 189 157 0 11 Feb 2021