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Airborne pollen grain detection from partially labelled data utilising semi-supervised learning
Benjamin Jin
, Manuel Milling
, Maria Pilar Plaza
*
, Jens O Brunner
, Claudia Traidl-Hoffmann
, Björn W Schuller
, Athanasios Damialis
*
*
Corresponding author af dette arbejde
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Keyphrases
Semi-supervised Learning
100%
Partially Labeled Data
100%
Grain Detection
100%
Pollen Grains
100%
Airborne Pollen
100%
Manual Testing
66%
Pollen Detection
66%
Microscope Image
66%
Pollen Monitoring
66%
Closing the Gap
33%
Best Model
33%
Respiratory Allergy
33%
Real-time Performance
33%
Detection Performance
33%
Deep Learning Algorithm
33%
Labeled Data
33%
Automatic Pollen Monitoring
33%
Forensic Application
33%
Partially Labeled
33%
Partial Labels
33%
Historic Climate
33%
Contrast Detection
33%
Deep Neural Network
33%
Image Generation
33%
Manual Correction
33%
Object Detector
33%
Manual Procedures
33%
Student Teachers
33%
Pollen Classification
33%
Generation Process
33%
Bounding Box
33%
Semi-supervised Method
33%
F1 Score
33%
Automated Procedure
33%
Self-supervised Pre-training
33%
Current Climate Change
33%
Pseudo-labeling
33%
Training Scheme
33%
Network Object
33%
Computer Science
Microscope Image
100%
Semisupervised Learning
100%
Gold Standard
50%
Use Case
50%
Contrast Detection
50%
Image Synthesis
50%
Detection Performance
50%
Deep Neural Network
50%
Forensic Application
50%
Object Detector
50%
Climatic Change
50%
Deep Learning Method
50%
Earth and Planetary Sciences
Supervised Learning
100%
Pollen
100%
Climate Change
8%
Real Time
8%
Immunology and Microbiology
Pollen
100%
Taxon
25%
Respiratory Tract Allergy
8%