  Style transfer describes the rendering of an image semantic content as
different artistic styles. Recently, generative adversarial networks (GANs)
have emerged as an effective approach in style transfer by adversarially
training the generator to synthesize convincing counterfeits. However,
traditional GAN suffers from the mode collapse issue, resulting in unstable
training and making style transfer quality difficult to guarantee. In addition,
the GAN generator is only compatible with one style, so a series of GANs must
be trained to provide users with choices to transfer more than one kind of
style. In this paper, we focus on tackling these challenges and limitations to
improve style transfer. We propose adversarial gated networks (Gated GAN) to
transfer multiple styles in a single model. The generative networks have three
modules: an encoder, a gated transformer, and a decoder. Different styles can
be achieved by passing input images through different branches of the gated
transformer. To stabilize training, the encoder and decoder are combined as an
autoencoder to reconstruct the input images. The discriminative networks are
used to distinguish whether the input image is a stylized or genuine image. An
auxiliary classifier is used to recognize the style categories of transferred
images, thereby helping the generative networks generate images in multiple
styles. In addition, Gated GAN makes it possible to explore a new style by
investigating styles learned from artists or genres. Our extensive experiments
demonstrate the stability and effectiveness of the proposed model for
multistyle transfer.
