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BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning
Jingfeng Zhang
, Bo Song
,
Haohan Wang
, Bo Han
, Tongliang Liu
, Lei Liu
, Masashi Sugiyama
School of Information Sciences
National Center for Supercomputing Applications (NCSA)
Research output
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peer-review
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Keyphrases
Noisy Label Learning
100%
Learning Algorithm
50%
Noise Type
37%
Noisy Labels
37%
Label Noise
37%
Learning Methods
25%
Value Loss
25%
Clean Label
25%
Source Code
12%
Small Sets
12%
Labeled Data
12%
Semi-supervised Learning
12%
Training Data
12%
Learning Objectives
12%
Generalization Performance
12%
New Dataset
12%
Novel Labels
12%
Large Margin
12%
Model Generalization
12%
Instance-dependent Noise
12%
Label Flipping Attack
12%
Data with Imperfect Labels
12%
Computer Science
Learning Algorithm
100%
Existing Label
50%
Experimental Result
25%
Training Data
25%
Semisupervised Learning
25%
Generalization Performance
25%
Physics
Noise Type
100%
Supervised Learning
33%