A Review of Unsupervised Learning Architectures and Framework for Visual Data

Abstract

The Supervised learning is rapidly growing, in artificial intelligence and machine learning has been applied in data processing research. Many researchers have paid attention to supervised learning. In the last few years, there has been a growth towards to keep hold on unsupervised learning in research to improve performance in video pose estimation, detection, segmentation, sequencing images, and classification. It retains great success; when applying unsupervised learning for Computer Vision, Natural Language Processing, Networking, Visual data representation, Image Processing etc. The focus of this paper is to provide an overview of the architecture and framework of unsupervised learning in the domain of visual data in previous published papers and their benefits.