SwitchGAN for multi-domain facial image translation

Recent studies for multi-domain facial image translation have achieved an impressive performance. However, the existing methods still have limitations for some tasks, such as translating a facial image into different age groups, or translating a facial expression into other expressions. To address this problem, we propose a Switch Generative Adversarial Network (SwitchGAN) to perform delicate image translation among multiple domains. A feature switching operation is proposed to achieve features selection and fusion in our conditional modules. Experiments on Morph, RaFD and CelebA databases show that our SwitchGAN can achieve visually better translation effects than StarGAN. The attribute classification results using the trained ResNet-18 classifier also quantitatively suggest that the face images generated by SwitchGAN achieved much higher accuracy than that generated by StarGAN.