A Survey on Generative Adversarial Networks for Imbalance Problems in Computer Vision Tasks

Abstract

Any computer vision application development starts off by acquiring images and data, then preprocessingand pattern recognition steps to perform a task. When the acquired image is highly imbalanced and notadequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems inacquired image datasets in certain complex real-world problems such as anomaly detection, emotionrecognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction,etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when thetraining dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gainedimmense attention by researchers across a variety of application domains due to their capability to modelcomplex real-world image data. It is particularly important that GANs can not only be used to generatesynthetic images, but also its fascinating adversarial learning idea showed good potential in restoringbalance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressingimbalance problems in image data. The real-world challenges and implementations of synthetic imagegeneration based on GANs are extensively covered in this survey. Our survey first introduces variousimbalance problems in computer vision tasks and its existing solutions, and then examine key conceptssuch as deep generative image models and GANs. After that, we propose taxonomy to summarize GANsbased techniques for addressing imbalance problems in computer vision tasks into three major categories:Image level imbalances in classification, object level imbalances in object detection and pixel levelimbalances in segmentation tasks. We elaborate the imbalance problems of each group, and furtherprovide GANs based solutions in each group. Readers will understand how GANs based techniques canhandle the problem of imbalances and boost performance of the computer vision algorithms.