论文标题
结构化甘斯
Structured GANs
论文作者
论文摘要
我们提出生成的对抗网络（GAN），其中控制了生成图像的对称属性。这是通过发电机网络的体系结构获得的，而训练过程和损失保持不变。将对称gan应用于面部图像合成，以生成具有不同对称性的新面孔。我们还提出了无监督的面部旋转能力，该功能基于新颖的一声微调概念。
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.
