Abstract
Generative Adversarial Network (GAN) is an exciting innovation in machine learning within the neural network field. These models are able to generate a realistic image, video or even voice output. One of the useful applications is its possibility to enrich data sets for better learning of neural network models. In the presented work, we focus on image augmentation with the use of several variations of GAN to improve the classification of convolutional neural network. Accordingly, to prove the advantage of GAN-based image augmentation in comparison with methods of classical augmentation, we used specifically three different degrees of image rotation and compared classification results of convolutional neural network that use images from these augmentation methods. Mentioned methods of image augmentation are applied to five datasets belonging to three different domains, specifically medical, astronomical and geological domain. The architecture and settings of the convolutional neural network are the same for all datasets. To evaluate classification results, we used confusion matrix, accuracy, precision, recall and F1-score.