IGAN: Inferent and Generative Adversarial Networks

• URL: http://arxiv.org/abs/2109.13360v1
• Date: Mon, 27 Sep 2021 21:48:35 GMT
• Title: IGAN: Inferent and Generative Adversarial Networks
• Authors: Dr. Luc Vignaud (ONERA, The French Aerospace Lab, France)
• Abstract summary: IGAN learns both a generative and an inference model on a complex high dimensional data distribution. It extends the traditional GAN framework with inference by rewriting the adversarial strategy in both the image and the latent space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: I present IGAN (Inferent Generative Adversarial Networks), a neural architecture that learns both a generative and an inference model on a complex high dimensional data distribution, i.e. a bidirectional mapping between data samples and a simpler low-dimensional latent space. It extends the traditional GAN framework with inference by rewriting the adversarial strategy in both the image and the latent space with an entangled game between data-latent encoded posteriors and priors. It brings a measurable stability and convergence to the classical GAN scheme, while keeping its generative quality and remaining simple and frugal in order to run on a lab PC. IGAN fosters the encoded latents to span the full prior space: this enables the exploitation of an enlarged and self-organised latent space in an unsupervised manner. An analysis of previously published articles sets the theoretical ground for the proposed algorithm. A qualitative demonstration of potential applications like self-supervision or multi-modal data translation is given on common image datasets including SAR and optical imagery.

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