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Robust Active Label Correction
Jan Kremer, Fei Sha,
Christian Igel
Datalogisk Institut
4
Citationer (Scopus)
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Keyphrases
Loss Function
100%
Training Pattern
33%
Additional Measurement
33%
Real-world Application
33%
Classification Performance
33%
Active Learning Strategies
33%
Image Classification
33%
Crowdsourcing
33%
Correction Algorithm
33%
Noise Rate
33%
Importance Weighting
33%
Noise Model
33%
Learning Performance
33%
Latent Variables
33%
Noisy Labels
33%
Delabeling
33%
Maximum a Posteriori Estimation
33%
Maximum Likelihood Estimation
33%
Human Expert
33%
Convolutional Neural Network
33%
Sampling Bias
33%
Noisy Data
33%
Earth and Planetary Sciences
Image Classification
100%
Sampling Bias
100%
Computer Science
Correction Algorithm
100%
Considered Problem
100%
Physics
Maximum Likelihood Estimate
100%
Image Classification
100%
Engineering
Maximum a Posteriori
33%
Human Expert
33%
Maximum Likelihood Estimate
33%
Real World Application
33%