XOGAN: One-to-Many Unsupervised Image-to-Image Translation
This addresses the need for generating varied outputs in unpaired image translation tasks, such as edges-to-objects and facial image translation, though it appears incremental by building on existing GAN-based translation frameworks.
The paper tackles the one-to-many unsupervised image-to-image translation problem, where an input image can map to multiple outputs, and proposes XOGAN to learn this relationship with an additional variable for controlling variations like color and texture, resulting in more diverse and plausible generated images compared to state-of-the-art methods that often produce monotonous colors.
Unsupervised image-to-image translation aims at learning the relationship between samples from two image domains without supervised pair information. The relationship between two domain images can be one-to-one, one-to-many or many-to-many. In this paper, we study the one-to-many unsupervised image translation problem in which an input sample from one domain can correspond to multiple samples in the other domain. To learn the complex relationship between the two domains, we introduce an additional variable to control the variations in our one-to-many mapping. A generative model with an XO-structure, called the XOGAN, is proposed to learn the cross domain relationship among the two domains and the ad- ditional variables. Not only can we learn to translate between the two image domains, we can also handle the translated images with additional variations. Experiments are performed on unpaired image generation tasks, including edges-to-objects translation and facial image translation. We show that the proposed XOGAN model can generate plausible images and control variations, such as color and texture, of the generated images. Moreover, while state-of-the-art unpaired image generation algorithms tend to generate images with monotonous colors, XOGAN can generate more diverse results.