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Poster
Multi-mapping Image-to-Image Translation via Learning Disentanglement
Xiaoming Yu · Yuanqi Chen · Shan Liu · Thomas Li · Ge Li

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #88
in Applications -- Computer Vision »

Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation. However, the existing methods only consider one of the two perspectives, which makes them unable to solve each other's problem. To address this issue, we propose a novel unified model, which bridges these two objectives. First, we disentangle the input images into the latent representations by an encoder-decoder architecture with a conditional adversarial training in the feature space. Then, we encourage the generator to learn multi-mappings by a random cross-domain translation. As a result, we can manipulate different parts of the latent representations to perform multi-modal and multi-domain translations simultaneously. Experiments demonstrate that our method outperforms state-of-the-art methods.

Keywords: Computer Vision · Generative Models · Applications · Algorithms -> Unsupervised Learning; Deep Learning -> Adversarial Networks; Deep Learning
[ Paper]  [ Poster] 

Author Information

Xiaoming Yu (Peking University)
Yuanqi Chen (SECE, Peking University)
Shan Liu (Tencent)
Thomas Li (Shenzhen Graduate School, Peking University)
Ge Li (SECE, Shenzhen Graduate School, Peking University)

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