  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.
