Study of Deep Learning Approach for Improving Semi-Supervised Algorithm

Abstract

When trained on the extensive collections of labeled data, Deep neural networks have demonstrated their potential for a good variety of supervised learning activities to deliver impressive performance. However, it takes a significant amount of money, time, and energy to construct such large data sets. In many practical situations, such tools might not be available, restricting the adoption and implementing many deep learning methods. Deep neural network-based semi-supervised learning implementations increasingly engaged in research to scale down the amount of labeled data needed, either by creating new methods or introducing current semi-supervised deep learning frameworks. To beat the need for giant annotated datasets, for more data-efficient deep learning methods. This article concentrated on a detailed analysis of semi-supervised learning focused on deep learning, accompanied by its description.