Current state-of-the-art conditional generative adversarial networks (C-GANs) require strong supervision via labeled datasets in order to generate images with continuously adjustable, disentangled semantics. In this paper we introduce a new formulation of the C-GAN that is able to learn realistic models with continuous, semantically meaningful input parameters and which has the advantage of requiring only the weak supervision of binary attribute labels. We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. The method allows continuous control over the attributes in synthesised images where precise labels are not readily available. For example, given only labels found using a simple classifier of ambient non-ambient lighting in images, IVI has enabled us to learn a generative face-image model with controllable lighting that is disentangled from other factors in the synthesised images, such as the identity. We evaluate IVI-GAN on the CelebA and CelebA-HQ datasets, learning to disentangle attributes such as lighting, pose, expression and age, and provide a quantitative comparison of IVI-GAN with a classical continuous C-GAN.
@cite_6: 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.
Another method with similar objectives is @cite_6 . In this work the GAN's generator is trained as a siamese network in which two images are generated from the same random identity'' vector but from different random observation vectors''. The GAN's discriminator then decides if the pair of images looks real compared pairs of real training images depicting the same identity but in different situations. Again, the network relies on the discriminator's capacity to learn to match identities between images in order to separate identity information from extraneous information. There is nothing preventing residual identity information from becoming encoded in the observation vectors.