Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation

Abstract

Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an image of a target domain without a dataset on it. We propose a method that expands the concept of `multi-domain' from data to the loss area and learns the combined characteristics of each domain to dynamically infer translations of images in mixed domains. First, we introduce Sym-parameter and its learning method for variously mixed losses while synchronizing them with input conditions. Then, we propose Sym-parameterized Generative Network (SGN) which is empirically confirmed of learning mixed characteristics of various data and losses, and translating images to any mixed-domain without ground truths, such as 30% Van Gogh and 20% Monet and 40% snowy.

16pages, This paper is accepted at ICCV 2019

References in corpus (9)

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Toward Multimodal Image-to-Image Translation
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Invertible Conditional GANs for image editing
• Unsupervised Cross-Domain Image Generation
• CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms