We present Generative Adversarial Networks (GANs), in which the symmetric
property of the generated images is controlled. This is obtained through the
generator network's architecture, while the training procedure and the loss
remain the same. The symmetric GANs are applied to face image synthesis in
order to generate novel faces with a varying amount of symmetry. We also
present an unsupervised face rotation capability, which is based on the novel
notion of one-shot fine tuning