A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation

Understand

We present a novel and unified deep learning framework which is capable of learning domain-invariant representation from data across multiple domains.

• Realized by adversarial training with additional ability to exploit domain-specific information, the proposed network is able to perform continuous cross-domain image translation and manipulation, and produces desirable output images accordingly.
• In addition, the resulting feature representation exhibits superior performance of unsupervised domain adaptation, which also verifies the effectiveness of the proposed model in learning disentangled features for describing cross-domain data.