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1803.11364
Cited By
Joint Optimization Framework for Learning with Noisy Labels
30 March 2018
Daiki Tanaka
Daiki Ikami
T. Yamasaki
Kiyoharu Aizawa
NoLa
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Papers citing
"Joint Optimization Framework for Learning with Noisy Labels"
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Noisy Annotation Refinement for Object Detection
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Clean or Annotate: How to Spend a Limited Data Collection Budget
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Continual Learning on Noisy Data Streams via Self-Purified Replay
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Breaking the Dilemma of Medical Image-to-image Translation
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Chenyu Lian
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13 Oct 2021
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
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Kangning Liu
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Yiqiu Shen
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256
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Weak Novel Categories without Tears: A Survey on Weak-Shot Learning
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Consistency Regularization Can Improve Robustness to Label Noise
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Hossein Azizpour
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Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
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Kaize Ding
Dingcheng Li
Alexander Hanbo Li
Xing Fan
Chenlei Guo
Yang Liu
Huan Liu
222
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0
25 Sep 2021
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
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Ruirui Li
Chuan Sun
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114
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0
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Assessing the Quality of the Datasets by Identifying Mislabeled Samples
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Gaurav Nuti
Yash Kumar Atri
Tanmoy Chakraborty
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213
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0
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MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces
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Guoqing Zheng
Ahmed Hassan Awadallah
108
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0
09 Sep 2021
Learning Fast Sample Re-weighting Without Reward Data
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Zizhao Zhang
Tomas Pfister
159
103
0
07 Sep 2021
NGC: A Unified Framework for Learning with Open-World Noisy Data
IEEE International Conference on Computer Vision (ICCV), 2021
Zhi-Fan Wu
Tong Wei
Jianwen Jiang
Chaojie Mao
Mingqian Tang
Yu-Feng Li
209
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0
25 Aug 2021
Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Yangdi Lu
Yang Bo
Wenbo He
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208
11
0
18 Aug 2021
Co-learning: Learning from Noisy Labels with Self-supervision
ACM Multimedia (ACM MM), 2021
Cheng Tan
Jun Xia
Lirong Wu
Stan Z. Li
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402
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When does loss-based prioritization fail?
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Xinyu Hu
Rosanne Liu
Sara Hooker
J. Yosinski
831
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Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
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Jialu Wang
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217
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0
13 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
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133
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08 Jul 2021
Understanding and Improving Early Stopping for Learning with Noisy Labels
Neural Information Processing Systems (NeurIPS), 2021
Ying-Long Bai
Erkun Yang
Bo Han
Yanhua Yang
Jiatong Li
Yinian Mao
Gang Niu
Tongliang Liu
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213
266
0
30 Jun 2021
Adaptive Sample Selection for Robust Learning under Label Noise
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282
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INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
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69
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Distilling effective supervision for robust medical image segmentation with noisy labels
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
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Ji Wu
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92
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0
21 Jun 2021
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
Neural Information Processing Systems (NeurIPS), 2021
Jianguo Huang
Lue Tao
Renchunzi Xie
Bo An
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286
101
0
21 Jun 2021
Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion
Xian-Jin Gui
Wei Wang
Zhang-Hao Tian
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124
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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
337
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0
14 Jun 2021
Salvage of Supervision in Weakly Supervised Object Detection
Computer Vision and Pattern Recognition (CVPR), 2021
Lin Sui
Chen-Da Liu-Zhang
Jianxin Wu
197
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08 Jun 2021
Generalized Domain Adaptation
Computer Vision and Pattern Recognition (CVPR), 2021
Yu Mitsuzumi
Go Irie
Daiki Ikami
Takashi Shibata
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168
23
0
03 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
269
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Instance Correction for Learning with Open-set Noisy Labels
Xiaobo Xia
Tongliang Liu
Bo Han
Biwei Huang
Jun Yu
Gang Niu
Masashi Sugiyama
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116
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0
01 Jun 2021
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
International Conference on Learning Representations (ICLR), 2021
Xiaobo Xia
Tongliang Liu
Bo Han
Biwei Huang
Jun Yu
Gang Niu
Masashi Sugiyama
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195
129
0
01 Jun 2021
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness
Glenn Dawson
R. Polikar
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298
4
0
28 May 2021
Training Classifiers that are Universally Robust to All Label Noise Levels
IEEE International Joint Conference on Neural Network (IJCNN), 2021
Jingyi Xu
Tony Q.S. Quek
Kai Fong Ernest Chong
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126
3
0
27 May 2021
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
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Shuo Yang
Erkun Yang
Bo Han
Yang Liu
Min Xu
Gang Niu
Tongliang Liu
NoLa
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273
54
0
27 May 2021
Generation and Analysis of Feature-Dependent Pseudo Noise for Training Deep Neural Networks
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Sree Ram Kamabattula
Kumudha Musini
Babak Namazi
G. Sankaranarayanan
V. Devarajan
NoLa
88
0
0
22 May 2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
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Erik Englesson
Hossein Azizpour
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417
132
0
10 May 2021
Faster Meta Update Strategy for Noise-Robust Deep Learning
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Youjiang Xu
Linchao Zhu
Lu Jiang
Yi Yang
171
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Boosting Co-teaching with Compression Regularization for Label Noise
Yingyi Chen
Xin Shen
S. Hu
Johan A. K. Suykens
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157
55
0
28 Apr 2021
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2021
Aritra Ghosh
Andrew Lan
NoLa
194
12
0
19 Apr 2021
Contrastive Learning Improves Model Robustness Under Label Noise
Aritra Ghosh
Andrew Lan
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179
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Joint Negative and Positive Learning for Noisy Labels
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Youngdong Kim
Juseung Yun
Hyounguk Shon
Junmo Kim
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161
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0
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Learning from Noisy Labels via Dynamic Loss Thresholding
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
Hao Yang
Youzhi Jin
Zi-Hua Li
Deng-Bao Wang
Lei Miao
Xin Geng
Min-Ling Zhang
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AI4CE
141
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0
01 Apr 2021
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training
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Changick Kim
220
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0
01 Apr 2021
Divergence Optimization for Noisy Universal Domain Adaptation
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Atsushi Hashimoto
Yoshitaka Ushiku
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118
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Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning
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107
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Friends and Foes in Learning from Noisy Labels
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Transform consistency for learning with noisy labels
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Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2020
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335
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Jo-SRC: A Contrastive Approach for Combating Noisy Labels
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218
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24 Mar 2021
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