Application of Generative Adversarial Network to Recognition of Ship Targets

Abstract

In this study Generative Adversarial Network(GAN) will be used for data augmentation applications to achieve the purpose of improving model performance In the study we used the WACGAN-GP which was formed by combining the advantages of ACGAN and WGAN-GP to apply data augmentation with only a small amount of training data The research method is to use GAN to generate data and add it to the original training data to perform data augmentation actions to improve the accuracy of model and achieve the effect of data augmentation In the study experiments were performed on five image categories of the visual image (optical image) MNIST dataset CIFAR-10 dataset ship classification dataset and non-visual image (synthetic aperture radar image) MSTAR dataset and OpenSARShip dataset Experiments on above datasets show that the accuracy rate after GAN's data augmentation has improved It also compares the effect difference with traditional data augmentation and the result is that WACGAN-GP is better so it also proves that GAN's Data augmentation is a direction worth studying in the future

Date of Award	2020
Original language	English
Supervisor	Kun-Chou Lee (Supervisor)