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Auxiliary image regularization for deep cNNs with noisy labels
Samaneh Azadi
, Jiashi Feng
,
Stefanie Jegelka
, Trevor Darrell
University of California at Berkeley
National University of Singapore
Massachusetts Institute of Technology
Research output
:
Contribution to conference
›
Paper
›
peer-review
28
Scopus citations
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Keyphrases
Alternating Direction multiplier Method
50%
Amateur
50%
Benchmark Dataset
50%
Cnns
100%
Comprehensive Experiment
50%
Contextual Information
50%
Convolutional Neural Network Model
100%
Deep Convolutional Neural Network (deep CNN)
100%
Image Classification
50%
Image Data
50%
Image Regularization
100%
Label Noise
50%
Labeled Data
50%
Labeled Sample
50%
Learning Process
50%
Mislabeling
50%
Noisy Labels
100%
Real Image
50%
Regularization Method
50%
Training Data
50%
Training Image
50%
Training Samples
50%
Computer Science
Alternating Direction Method of Multipliers
33%
Context Information
33%
Convolutional Neural Network
66%
Deep Convolutional Neural Networks
66%
Image Classification
33%
Labeled Data Set
33%
Learning Process
33%
Neural Network Model
100%
Regularization
100%
Training Data
33%
Training Image
33%
Training Sample
33%
Chemical Engineering
Auxiliaries
100%
Neural Network
100%