XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains.We introduce XGAN, a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space.We report promising qualitative results for the task of face-to-cartoon translation.The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.
XGAN is an unsupervised model for feature-level image-to-image translation applied to semantic style transfer problems such as the face-to-cartoon task, for which we introduce a new dataset.This paper proposes a new GAN-based model for unpaired image-to-image translation similar to DTN