Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN

• URL: http://arxiv.org/abs/2112.09653v1
• Date: Fri, 17 Dec 2021 17:56:30 GMT
• Title: Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
• Authors: Vitaliy Kinakh, Mariia Drozdova, Guillaume Qu\'etant, Tobias Golling, Slava Voloshynovskiy
• Abstract summary: We present a contrastive conditional generative adversarial network (Info SCC-GAN) with an explorable latent space. Info SCC-GAN is derived based on an information-theoretic formulation of mutual information between input data and latent space representation. Experiments show that Info SCC-GAN outperforms the "vanilla" EigenGAN in the image generation on AFHQ and CelebA datasets.
• Score: 6.201770337181472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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