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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

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.

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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