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J. KIMS Technol > Volume 22(1); 2019 > Article
Journal of the Korea Institute of Military Science and Technology 2019;22(1):49-59.
DOI: https://doi.org/10.9766/KIMST.2019.22.1.049   
Synthetic Image Dataset Generation for Defense using Generative Adversarial Networks
Hunmin Yang
Institute of Defense Advanced Research, Agency for Defense Development
국방용 합성이미지 데이터셋 생성을 위한 대립훈련신경망 기술 적용 연구
국방과학연구소 국방고등기술원
Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.
Key Words: Generative Adversarial Networks, Synthetic Image, Deep Learning, Machine Learning, Dataset


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