Any computer vision application development starts off by acquiring images and data, then preprocessing
and pattern recognition steps to perform a task. When the acquired image is highly imbalanced and not
adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in
acquired image datasets in certain complex real-world problems such as anomaly detection, emotion
recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction,
etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the
training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained
immense attention by researchers across a variety of application domains due to their capability to model
complex real-world image data. It is particularly important that GANs can not only be used to generate
synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring
balance in imbalanced datasets.
In this paper, we examine the most recent developments of GANs based techniques for addressing
imbalance problems in image data. The real-world challenges and implementations of synthetic image
generation based on GANs are extensively covered in this survey. Our survey first introduces various
imbalance problems in computer vision tasks and its existing solutions, and then examine key concepts
such as deep generative image models and GANs. After that, we propose taxonomy to summarize GANs
based 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 level
imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and further
provide GANs based solutions in each group. Readers will understand how GANs based techniques can
handle the problem of imbalances and boost performance of the computer vision algorithms.