Conditional GAN for Small Datasets
Komei Hiruta, Ryusuke Saito, Taro Hatakeyama, Atsushi Hashimoto, Satoshi Kurihara · 2022
Generating high-quality images with Generative Adversarial Networks (GANs) generally requires 100k+ training data. The required data amount is too large when we consider using GANs to support professional art creators; they need to follow the specific art style while interactively controlling the results along with their theme. This research proposes Conditional FastGAN, which adds a condition vector to FastGAN to produce high-quality different domain images even on small datasets. In our experiments, the MUCT Face Database of images consisting of face photos in various orientations and manga face images extracted from Osamu Tezuka’s works were used as a small-scale dataset. Fine-tuning with manga face images to a model pre-trained with photo-only face images enabled control of the generated images according to explicit conditions, such as photos and manga, for the same latent variables. In addition, the proposed method improved the FID score by 2.55 from the original FastGAN in the case of manga face generation.