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

Paper summary: The authors propose a VAE-GAN architecture for learning multi-domain image representations where the domain information is disentangled from the domain invariant information, while simultaneously generating images of all considered domains. A domain classifier D_v pushes the VAE encoder E to learn a domain-invariant embedding z by adversarial training. This embedding is then concatenated with a one-hot encoding v of the domain before being fed to the VAE decoder G, which is then able to reconstruct domain-appropriate images. The VAE is further paired with an adversarial real / fake classifier, which improves image sharpness, and a domain classifier, which promotes mutual information between the generated images and the domain encoding v. Experiments are performed on human face generation and semi-supervised digits classification. Comments: The proposed approach is pretty straightforward when compared to recent works on image generation / feature disentanglement (which is good), and the "unified" nature of the proposed architecture must be appreciated.