We propose a new algorithm for training generative adversarial networks that
jointly learns latent codes for both identities (e.g. individual humans) and
observations (e.g. specific photographs). By fixing the identity portion of the
latent codes, we can generate diverse images of the same subject, and by fixing
the observation portion, we can traverse the manifold of subjects while
maintaining contingent aspects such as lighting and pose. Our algorithm
features a pairwise training scheme in which each sample from the generator
consists of two images with a common identity code. Corresponding samples from
the real dataset consist of two distinct photographs of the same subject. In
order to fool the discriminator, the generator must produce pairs that are
photorealistic, distinct, and appear to depict the same individual. We augment
both the DCGAN and BEGAN approaches with Siamese discriminators to facilitate
pairwise training. Experiments with human judges and an off-the-shelf face
verification system demonstrate our algorithm's ability to generate convincing,
identity-matched photographs.

本研究提出一种新的算法用于训练生成式对抗网络，并联合学习身份（例如个人）和观察（例如具体照片）的潜在代码。我们使用成对的训练模式，可生成多样的相同主题图像，并保持灯光和姿势等相关方面的特征，进而提高图像的真实感。结果表明，基于 Siamese 辨别器进行的训练能够生成逼真的与身份相符的照片。