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We propose a new algorithm for training generative adversarial networks thatjointly learns latent codes for both identities (e.g. individual humans) andobservations (e.g. specific photographs). By fixing the identity portion of thelatent codes, we can generate diverse images of the same subject, and by fixingthe observation portion, we can traverse the manifold of subjects whilemaintaining contingent aspects such as lighting and pose. Our algorithmfeatures a pairwise training scheme in which each sample from the generatorconsists of two images with a common identity code. Corresponding samples fromthe real dataset consist of two distinct photographs of the same subject. Inorder to fool the discriminator, the generator must produce pairs that arephotorealistic, distinct, and appear to depict the same individual. We augmentboth the DCGAN and BEGAN approaches with Siamese discriminators to facilitatepairwise training. Experiments with human judges and an off-the-shelf faceverification system demonstrate our algorithm's ability to generate convincing,identity-matched photographs.

Semantically Decomposing the Latent Spaces of Generative Adversarial Networks