Formula-Driven Supervised Learning in Computer Vision: A Literature Survey

Abstract

Current computer vision research uses huge datasets with millions of images to pre-train vision models. This results in escalation of time and capital, ethical issues, moral issues, privacy issues, copyright issues, fairness issues, and others. To address these issues, several alternative learning schemes have been developed. One such scheme is formula-based supervised learning (FDSL). It is a form of supervised learning, which involves the use of mathematically generated images for the pre-training of deep models. Promising results have been obtained for computer-vision-related applications. In this comprehensive survey paper, a gentle introduction to FDSL is presented. The supporting theory, databases, experimentation and ensuing results are discussed. The research outcomes, issues and scope are also discussed. Finally, some of the most promising future directions for FDSL research are discussed. As FDSL is an important learning technique, this survey represents a useful resource for interested researchers working on solving various problem in computer vision and related areas of application.