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Joint Negative and Positive Learning for Noisy Labels

Joint Negative and Positive Learning for Noisy Labels

14 April 2021
Youngdong Kim
Juseung Yun
Hyounguk Shon
Junmo Kim
    NoLa
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Papers citing "Joint Negative and Positive Learning for Noisy Labels"

10 / 10 papers shown
Title
Learning with Noisy Labels: Interconnection of Two
  Expectation-Maximizations
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations
Heewon Kim
Hyun Sung Chang
Kiho Cho
Jaeyun Lee
Bohyung Han
NoLa
26
2
0
09 Jan 2024
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Jihye Kim
A. Baratin
Yan Zhang
Simon Lacoste-Julien
NoLa
18
7
0
03 Dec 2022
On Robust Learning from Noisy Labels: A Permutation Layer Approach
On Robust Learning from Noisy Labels: A Permutation Layer Approach
Salman Alsubaihi
Mohammed Alkhrashi
Raied Aljadaany
Fahad Albalawi
Guohao Li
NoLa
23
0
0
29 Nov 2022
Centrality and Consistency: Two-Stage Clean Samples Identification for
  Learning with Instance-Dependent Noisy Labels
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
Ganlong Zhao
Guanbin Li
Yipeng Qin
Feng Liu
Yizhou Yu
NoLa
33
22
0
29 Jul 2022
Learning from Data with Noisy Labels Using Temporal Self-Ensemble
Learning from Data with Noisy Labels Using Temporal Self-Ensemble
Jun Ho Lee
J. Baik
Taebaek Hwang
J. Choi
NoLa
28
1
0
21 Jul 2022
Weakly-Supervised Temporal Action Localization by Progressive
  Complementary Learning
Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning
Jiachen Du
Jialuo Feng
Kun-Yu Lin
Fa-Ting Hong
Xiao-Ming Wu
Zhongang Qi
Ying Shan
Weihao Zheng
37
5
0
22 Jun 2022
UNICON: Combating Label Noise Through Uniform Selection and Contrastive
  Learning
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
Nazmul Karim
Mamshad Nayeem Rizve
Nazanin Rahnavard
Ajmal Saeed Mian
M. Shah
NoLa
30
98
0
28 Mar 2022
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Yuchao Wang
Haochen Wang
Yujun Shen
Jingjing Fei
Wei Li
Guoqiang Jin
Liwei Wu
Rui Zhao
Xinyi Le
UQCV
22
331
0
08 Mar 2022
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
L2B: Learning to Bootstrap Robust Models for Combating Label Noise
Yuyin Zhou
Xianhang Li
Fengze Liu
Qingyue Wei
Xuxi Chen
Lequan Yu
Cihang Xie
M. Lungren
Lei Xing
NoLa
39
3
0
09 Feb 2022
Combating noisy labels by agreement: A joint training method with
  co-regularization
Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei
Lei Feng
Xiangyu Chen
Bo An
NoLa
319
498
0
05 Mar 2020
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