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Improved Naive Bayes with Mislabeled Data

Improved Naive Bayes with Mislabeled Data

13 April 2023
Qianhan Zeng
Yingqiu Zhu
Xuening Zhu
Feifei Wang
Weichen Zhao
Shuning Sun
Meng Su
Hansheng Wang
    NoLa
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Papers citing "Improved Naive Bayes with Mislabeled Data"

15 / 15 papers shown
Title
Pervasive Label Errors in Test Sets Destabilize Machine Learning
  Benchmarks
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G. Northcutt
Anish Athalye
Jonas W. Mueller
69
530
0
26 Mar 2021
Learning Noise Transition Matrix from Only Noisy Labels via Total
  Variation Regularization
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Yivan Zhang
Gang Niu
Masashi Sugiyama
NoLa
59
81
0
04 Feb 2021
Annotation-efficient deep learning for automatic medical image
  segmentation
Annotation-efficient deep learning for automatic medical image segmentation
Shanshan Wang
Cheng Li
Rongpin Wang
Zaiyi Liu
Meiyun Wang
...
Xin Liu
Jie Chen
Hui-Chong Zhou
Ismail Ben Ayed
Hairong Zheng
VLM
MedIm
62
184
0
09 Dec 2020
nuScenes: A multimodal dataset for autonomous driving
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar
Varun Bankiti
Alex H. Lang
Sourabh Vora
Venice Erin Liong
Qiang Xu
Anush Krishnan
Yuxin Pan
G. Baldan
Oscar Beijbom
3DPC
271
5,705
0
26 Mar 2019
Co-teaching: Robust Training of Deep Neural Networks with Extremely
  Noisy Labels
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han
Quanming Yao
Xingrui Yu
Gang Niu
Miao Xu
Weihua Hu
Ivor Tsang
Masashi Sugiyama
NoLa
104
2,062
0
18 Apr 2018
Deep Face Recognition: A Survey
Deep Face Recognition: A Survey
Mei Wang
Weihong Deng
NoLa
113
1,231
0
18 Apr 2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  on Corrupted Labels
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang
Zhengyuan Zhou
Thomas Leung
Li Li
Li Fei-Fei
NoLa
93
1,450
0
14 Dec 2017
Decoupling "when to update" from "how to update"
Decoupling "when to update" from "how to update"
Eran Malach
Shai Shalev-Shwartz
NoLa
72
565
0
08 Jun 2017
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
318
4,624
0
10 Nov 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction
  Approach
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini
A. Rozza
A. Menon
Richard Nock
Zhuang Li
NoLa
90
1,450
0
13 Sep 2016
A Unified View of Multi-Label Performance Measures
A Unified View of Multi-Label Performance Measures
Xi-Zhu Wu
Zhi Zhou
60
218
0
01 Sep 2016
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
Ira Kemelmacher-Shlizerman
S. M. Seitz
Daniel Miller
Evan Brossard
CVBM
76
859
0
02 Dec 2015
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
1.5K
39,472
0
01 Sep 2014
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in Context
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
375
43,524
0
01 May 2014
Identifying Mislabeled Training Data
Identifying Mislabeled Training Data
C. Brodley
M. Friedl
104
969
0
01 Jun 2011
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