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R Capital uses technologies and results of the work of StyleGAN on the website you visit here. This is the application of a generative adversarial network (GAN). It was introduced by developers from the company Nvidia in December 2018 and depends on the Nvidia software CUDA, GPUs and TensorFlow.

GANs are so-called Generative Adversarial Networks consisting of two artificial neural networks that perform a zero-sum game. One of them creates candidates (the generator), the second neural network evaluates the candidates (the discriminator). Typically, the generator maps from a vector of latent variables to the desired outcome space. The goal of the generator is to learn to produce results according to a particular distribution. The discriminator, on the other hand, is trained to distinguish the generator’s results from the data from the true, given distribution. The generator’s objective function is then to produce results that the discriminator cannot distinguish. In this way, the generated distribution should gradually converge to the true distribution.

GANs have been used to create photorealistic images to visualize various objects, faces, landscapes, as well as to model motion patterns in videos, to create 3D models of objects from 2D images, and to image process astronomical images, among others. GANs are also used for natural sensing in user interaction with chatbots. In addition, GAN applications are used in particle physics to speed up time-consuming detector simulations.