  Despite Generative Adversarial Networks (GANs) have been widely used in
various image-to-image translation tasks, they can be hardly applied on mobile
devices due to their heavy computation and storage cost. Traditional network
compression methods focus on visually recognition tasks, but never deal with
generation tasks. Inspired by knowledge distillation, a student generator of
fewer parameters is trained by inheriting the low-level and high-level
information from the original heavy teacher generator. To promote the
capability of student generator, we include a student discriminator to measure
the distances between real images, and images generated by student and teacher
generators. An adversarial learning process is therefore established to
optimize student generator and student discriminator. Qualitative and
quantitative analysis by conducting experiments on benchmark datasets
demonstrate that the proposed method can learn portable generative models with
strong performance.
