TITLE: YuruGAN: Yuru-Chara Mascot Generator Using Generative Adversarial Networks With Clustering Small Dataset 

ABSTRACT: A yuru-chara is a mascot character created by local governments and companies
for publicizing information on areas and products. Because it takes various
costs to create a yuruchara, the utilization of machine learning techniques
such as generative adversarial networks (GANs) can be expected. In recent
years, it has been reported that the use of class conditions in a dataset for
GANs training stabilizes learning and improves the quality of the generated
images. However, it is difficult to apply class conditional GANs when the
amount of original data is small and when a clear class is not given, such as a
yuruchara image. In this paper, we propose a class conditional GAN based on
clustering and data augmentation. Specifically, first, we performed clustering
based on K-means++ on the yuru-chara image dataset and converted it into a
class conditional dataset. Next, data augmentation was performed on the class
conditional dataset so that the amount of data was increased five times. In
addition, we built a model that incorporates ResBlock and self-attention into a
network based on class conditional GAN and trained the class conditional
yuru-chara dataset. As a result of evaluating the generated images, the effect
on the generated images by the difference of the clustering method was
confirmed.
A yuru-chara is a mascot character created by local governments and companies
for publicizing information on areas and products. Because it takes various
costs to create a yuruchara, the utilization of machine learning techniques
such as generative adversarial networks (GANs) can be expected. In recent
years, it has been reported that the use of class conditions in a dataset for
GANs training stabilizes learning and improves the quality of the generated
images. However, it is difficult to apply class conditional GANs when the
amount of original data is small and when a clear class is not given, such as a
yuruchara image. In this paper, we propose a class conditional GAN based on
clustering and data augmentation. Specifically, first, we performed clustering
based on K-means++ on the yuru-chara image dataset and converted it into a
class conditional dataset. Next, data augmentation was performed on the class
conditional dataset so that the amount of data was increased five times. In
addition, we built a model that incorporates ResBlock and self-attention into a
network based on class conditional GAN and trained the class conditional
yuru-chara dataset. As a result of evaluating the generated images, the effect
on the generated images by the difference of the clustering method was
confirmed.
YuruGAN: Yuru-Chara Mascot Generator Using Generative Adversarial Networks With Clustering Small Dataset
http://arxiv.org/abs/2004.08066v1