A Generative Model for Semi-Supervised Learning
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.
Committee: Jin Tian (major professor), Jia Liu, Wensheng Zhang