  Recent studies on multi-domain facial image translation have achieved
impressive results. The existing methods generally provide a discriminator with
an auxiliary classifier to impose domain translation. However, these methods
neglect important information regarding domain distribution matching. To solve
this problem, we propose a switch generative adversarial network (SwitchGAN)
with a more adaptive discriminator structure and a matched generator to perform
delicate image translation among multiple domains. A feature-switching
operation is proposed to achieve feature selection and fusion in our
conditional modules. We demonstrate the effectiveness of our model.
Furthermore, we also introduce a new capability of our generator that
represents attribute intensity control and extracts content information without
tailored training. Experiments on the Morph, RaFD and CelebA databases visually
and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can
achieve better translation results than StarGAN, AttGAN and STGAN. The
attribute classification accuracy achieved using the trained ResNet-18 model
and the FID score obtained using the ImageNet pretrained Inception-v3 model
also quantitatively demonstrate the superior performance of our models.
