Back to articles
Articles
Volume: 33 | Article ID: art00007
Image
EVALUATING DEEP SEMI-SUPERVISED LEARNING METHODS FOR COMPUTER VISION APPLICATIONS
  DOI :  10.2352/ISSN.2470-1173.2021.6.IRIACV-313  Published OnlineJanuary 2021
Abstract

Deep semi-supervised learning (SSL) have been significantly investigated in the past few years due to its broad spectrum of theory, algorithms, and applications. The extensive use of the SSL methods is dominant in the field of computer vision, for example, image classification, human activity recognition, object detection, scene segmentation, and image generation. In spite of the significant success achieved in these domains, critically analyzing SSL methods on benchmark datasets still presents important challenges. In the literature, very limited reviews and surveys are available. In this paper, we present short but focused review about the most significant SSL methods. We analyze the basic theory of SSL and the differences among various SSL methods. Then, we present experimental analysis to compare these SSL methods using standard datasets. We also provide an insight into the challenges of the SSL methods.

Subject Areas :
Views 33
Downloads 1
 articleview.views 33
 articleview.downloads 1
  Cite this article 

Habib Ullah, Mohib Ullah, Sultan Daud Khan, Faouzi Alaya Cheikh, "EVALUATING DEEP SEMI-SUPERVISED LEARNING METHODS FOR COMPUTER VISION APPLICATIONSin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2021,  pp 313-1 - 313-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.6.IRIACV-313

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA