NIPS Proceedingsβ

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

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

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Authors

Conference Event Type: Poster

Abstract

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.