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.
Similarly, Liu at al. REF proposed a UFDN that learns domain-invariant representation from multiple domains and can perform continuous cross-domain image translation and manipulation.
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
ArXiv
ArXiv
Computer Science