A Review of Generative Adversarial Networks for Computer Vision Tasks

Abstract

In recent years, computer vision tasks have gained a lot of popularity, accompanied by the development of numerous powerful architectures consistently delivering outstanding results when applied to well-annotated datasets. However, acquiring a high-quality dataset remains a challenge, particularly in sensitive domains like medical imaging, where expense and ethical concerns represent a challenge. Generative adversarial networks (GANs) offer a possible solution to artificially expand datasets, providing a basic resource for applications requiring large and diverse data. This work presents a thorough review and comparative analysis of the most promising GAN architectures. This review is intended to serve as a valuable reference for selecting the most suitable architecture for diverse projects, diminishing the challenges posed by limited and constrained datasets. Furthermore, we developed practical experimentation, focusing on the augmentation of a medical dataset derived from a colonoscopy video. We also applied one of the GAN architectures outlined in our work to a dataset consisting of histopathology images. The goal was to illustrate how GANs can enhance and augment datasets, showcasing their potential to improve overall data quality. Through this research, we aim to contribute to the broader understanding and application of GANs in scenarios where dataset scarcity poses a significant obstacle, particularly in medical imaging applications.