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

File Format

PDF

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