Degree Type
Creative Component
Semester of Graduation
Fall 2019
Department
Computer Science
First Major Professor
Jin Tian
Degree(s)
Master of Science (MS)
Major(s)
Computer Science
Abstract
Semi-Supervised learning is of great interest in a wide variety of research areas, including natural language processing, speech synthesizing, image classification, genomics etc. Semi-Supervised Generative Model is one Semi-Supervised learning approach that learns labeled data and unlabeled data simultaneously. A drawback of current Semi-Supervised Generative Models is that latent encoding learnt by generative models is concatenated directly with predicted label, which may result in degradation in representation learning. In this paper we present a new Semi-Supervised Generative Models that removes the direct dependency of data generation on label, hence overcomes this drawback. We show experiments that verifies this approach, together with comparison with existing works.
Copyright Owner
Shang Da
Copyright Year
2019
File Format
Recommended Citation
Da, Shang, "A Generative Model for Semi-Supervised Learning" (2019). Creative Components. 382.
https://lib.dr.iastate.edu/creativecomponents/382
