XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

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Authors Kevin Murphy, Amélie Royer, Fred Bertsch, Stephan Gouws, Konstantinos Bousmalis, Forrester Cole, Inbar Mosseri
Journal/Conference Name ICLR 2018 1
Paper Category
Paper Abstract 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 ("Cross-GAN"), 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, CartoonSet, we collected for this purpose is publicly available at google.github.io/cartoonset/ as a new benchmark for semantic style transfer.
Date of publication 2017
Code Programming Language Python
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