Authors: Jeongik Cho
StarGAN has shown excellent performance in image-to-image translation using adversarial, reconstruction, and classification losses in multi-domain image-to-image translation. The Style-Based Generator Architecture boosts generator performance through the Embedder and AdaIn modules. I propose here an attribute loss, which is like having multiple GANs, which is enhanced by combining StarGAN's conditional GAN loss (adversarial loss and classification loss) to improve learning speed. And suggest the new generator architecture, whose name is bi-directional progressive growing Style-Based U-Net generator, to improve learning speed.
Comments: 7 Pages.
[v1] 2019-09-03 20:56:00
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