Generating Custom Datasets with Multi Generative Adversarial Networks
Donghee Lee, Byeong-Woo Kim · International Journal of Computer Applications · 2022
Object detection and data collection from custom targets suffer from certain problems, which inherently occur in deep learning networks owing to problems such as difficulty of collection and data bias.Therefore, in this study, we proposed the Multi-GAN framework for generating augmented datasets.This framework comprises two parts: the first part generates data that reflect various textures related to decep learning based on deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN) structures.The second part provides multiple resolutions based on super-resolution GAN (SRGAN).Here, this paper presents efficient dataset construction methods along with a conventional augmentation method called manipulation technique.Through the experiments, which were based on average precision, conducted on the collected and augmented datasets, the proposed frameworkdemonstrated to improve detection accuracy.Additionally, we confirmed that the multi-GAN framework is superior with respect to efficiency to data collection.