One-shot style transfer using Wasserstein Autoencoder
Hidemoto Nakada, Hideki Asoh · 2021 Asian Conference on Innovation in Technology (ASIANCON) · 2021
We propose an image style transfer method based on disentangled representation obtained with Wasser-stein Autoencoder. Style transfer is an area of image generation technique that generates an image that shows content taken from one image with a style taken from another image. While there are extensive researches in this area, most of them require some ‘training’ time to generate images with a specific style. The proposed method does not require training time since we train a versatile network that can be used for any style and content image. The network encodes images into disentangled latent variables that represent content and style. We can transfer image style by simply replacing style latent variables. We tested the proposed method with images from CelebA and confirmed that it can generate style transferred images.