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A Second-Order Approach to Learning with Instance-Dependent Label Noise

A Second-Order Approach to Learning with Instance-Dependent Label Noise

22 December 2020
Zhaowei Zhu
Tongliang Liu
Yang Liu
    NoLa
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Papers citing "A Second-Order Approach to Learning with Instance-Dependent Label Noise"

29 / 29 papers shown
Title
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels
Yaxuan Wang
Hao Cheng
Jing Xiong
Qingsong Wen
Han Jia
Ruixuan Song
Li Zhang
Zhaowei Zhu
Yang Liu
AI4TS
58
1
0
21 Jan 2025
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
Mitigating the Impact of False Negatives in Dense Retrieval with
  Contrastive Confidence Regularization
Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization
Shiqi Wang
Yeqin Zhang
Cam-Tu Nguyen
20
2
0
30 Dec 2023
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy
  Labels
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
Chuanyan Hu
Shipeng Yan
Zhitong Gao
Xuming He
NoLa
24
4
0
20 Jun 2023
Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks
Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks
Emilie Grégoire
M. H. Chaudhary
Sam Verboven
24
1
0
07 Jun 2023
Instance-dependent Noisy-label Learning with Graphical Model Based
  Noise-rate Estimation
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Arpit Garg
Cuong C. Nguyen
Rafael Felix
Thanh-Toan Do
G. Carneiro
NoLa
33
1
0
31 May 2023
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei
Zhaowei Zhu
Gang Niu
Tongliang Liu
Sijia Liu
Masashi Sugiyama
Yang Liu
36
6
0
22 Mar 2023
When Source-Free Domain Adaptation Meets Learning with Noisy Labels
When Source-Free Domain Adaptation Meets Learning with Noisy Labels
L. Yi
Gezheng Xu
Pengcheng Xu
Jiaqi Li
Ruizhi Pu
Charles Ling
A. McLeod
Boyu Wang
23
39
0
31 Jan 2023
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy
  Labels
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels
Daehwan Kim
Kwang-seok Ryoo
Hansang Cho
Seung Wook Kim
NoLa
24
3
0
20 Nov 2022
Distributional Reward Estimation for Effective Multi-Agent Deep
  Reinforcement Learning
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning
Jifeng Hu
Yanchao Sun
Hechang Chen
Sili Huang
Haiyin Piao
Yi-Ju Chang
Lichao Sun
15
5
0
14 Oct 2022
Tackling Instance-Dependent Label Noise with Dynamic Distribution
  Calibration
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration
Manyi Zhang
Yuxin Ren
Zihao Wang
C. Yuan
21
3
0
11 Oct 2022
Instance-Dependent Noisy Label Learning via Graphical Modelling
Instance-Dependent Noisy Label Learning via Graphical Modelling
Arpit Garg
Cuong C. Nguyen
Rafael Felix
Thanh-Toan Do
G. Carneiro
NoLa
34
27
0
02 Sep 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
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature
  Entropy State
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Xinshao Wang
Yang Hua
Elyor Kodirov
S. Mukherjee
David A. Clifton
N. Robertson
19
6
0
30 Jun 2022
Towards Harnessing Feature Embedding for Robust Learning with Noisy
  Labels
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels
Chuang Zhang
Li Shen
Jian Yang
Chen Gong
NoLa
27
5
0
27 Jun 2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized
  Transition Matrix Estimation
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
De-Chun Cheng
Tongliang Liu
Yixiong Ning
Nannan Wang
Bo Han
Gang Niu
Xinbo Gao
Masashi Sugiyama
NoLa
39
65
0
06 Jun 2022
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation
Xiaoqing Guo
Jie Liu
Tongliang Liu
Yiyuan Yuan
38
27
0
29 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
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with
  Lower-Quality Features
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Zhaowei Zhu
Jialu Wang
Yang Liu
NoLa
35
37
0
02 Feb 2022
Open-Vocabulary Instance Segmentation via Robust Cross-Modal
  Pseudo-Labeling
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
Dat T. Huynh
Jason Kuen
Zhe-nan Lin
Jiuxiang Gu
Ehsan Elhamifar
ISeg
VLM
24
83
0
24 Nov 2021
Mitigating Memorization of Noisy Labels via Regularization between
  Representations
Mitigating Memorization of Noisy Labels via Regularization between Representations
Hao Cheng
Zhaowei Zhu
Xing Sun
Yang Liu
NoLa
38
28
0
18 Oct 2021
Detecting Corrupted Labels Without Training a Model to Predict
Detecting Corrupted Labels Without Training a Model to Predict
Zhaowei Zhu
Zihao Dong
Yang Liu
NoLa
149
62
0
12 Oct 2021
Adaptive Sample Selection for Robust Learning under Label Noise
Adaptive Sample Selection for Robust Learning under Label Noise
Deep Patel
P. Sastry
OOD
NoLa
28
29
0
29 Jun 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei
Hangyu Liu
Tongliang Liu
Gang Niu
Masashi Sugiyama
Yang Liu
NoLa
32
69
0
08 Jun 2021
Learning from Noisy Labels via Dynamic Loss Thresholding
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
26
6
0
01 Apr 2021
Clusterability as an Alternative to Anchor Points When Learning with
  Noisy Labels
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Zhaowei Zhu
Yiwen Song
Yang Liu
NoLa
13
91
0
10 Feb 2021
Provably End-to-end Label-Noise Learning without Anchor Points
Provably End-to-end Label-Noise Learning without Anchor Points
Xuefeng Li
Tongliang Liu
Bo Han
Gang Niu
Masashi Sugiyama
NoLa
133
120
0
04 Feb 2021
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
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Antonin Berthon
Bo Han
Gang Niu
Tongliang Liu
Masashi Sugiyama
NoLa
31
104
0
11 Jan 2020
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