  Conditional generation is a subclass of generative problems where the output
of the generation is conditioned by the attribute information. In this paper,
we present a stochastic contrastive conditional generative adversarial network
(InfoSCC-GAN) with an explorable latent space. The InfoSCC-GAN architecture is
based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an
attribute classifier and an EigenGAN generator. We propose a novel training
method, based on generator regularization using external or internal attributes
every $n$-th iteration, using a pre-trained contrastive encoder and a
pre-trained classifier. The proposed InfoSCC-GAN is derived based on an
information-theoretic formulation of mutual information maximization between
input data and latent space representation as well as latent space and
generated data. Thus, we demonstrate a link between the training objective
functions and the above information-theoretic formulation. The experimental
results show that InfoSCC-GAN outperforms the "vanilla" EigenGAN in the image
generation on AFHQ and CelebA datasets. In addition, we investigate the impact
of discriminator architectures and loss functions by performing ablation
studies. Finally, we demonstrate that thanks to the EigenGAN generator, the
proposed framework enjoys a stochastic generation in contrast to vanilla
deterministic GANs yet with the independent training of encoder, classifier,
and generator in contrast to existing frameworks. Code, experimental results,
and demos are available online at https://github.com/vkinakh/InfoSCC-GAN.

Computer Vision and Pattern Recognition;Machine Learning