Augmentation Learning for Semi-Supervised Classification

Tim Frommknecht; Pedro Alves Zipf; Quanfu Fan; Nina Shvetsova; Hilde Kuehne

doi:10.1007/978-3-031-16788-1_6

ScienceGate Book Chapters

BOOK-CHAPTER

Augmentation Learning for Semi-Supervised Classification

Tim Frommknecht Pedro Alves Zipf Quanfu Fan Nina Shvetsova Hilde Kuehne

Year: 2022 Lecture notes in computer science Pages: 85-98 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-16788-1_6

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Pipeline (software) Machine learning Artificial intelligence Semi-supervised learning Domain (mathematical analysis) Supervised learning Labeled data Training set Artificial neural network Mathematics

Metrics

Cited By

0.36

FWCI (Field Weighted Citation Impact)

Refs

0.60

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Multimodal Machine Learning Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Neural Network Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Augmentation Learning for Semi-Supervised Classification

Metrics

Citation History

Topics

Related Documents

Local augmentation based consistency learning for semi-supervised pathology image classification

Semi-supervised node classification via fine-grained graph auxiliary augmentation learning

NodeAug: Semi-Supervised Node Classification with Data Augmentation

Generative Adversarial learning with Negative Data Augmentation for Semi-supervised Text Classification

Semi-supervised learning using hidden feature augmentation