TRAINING GAN (GENERATIVE ADVERSARIAL NETWORKS) TO CREATE PIXEL-BY-PIXEL ANNOTATION Russian patent published in 2020 - IPC G06N3/08 G06N20/00 

FIELD: physics.

SUBSTANCE: invention relates to a method and a computer-readable medium for combined synthesis of images and pixel-by-pixel annotations for display. Method comprises preliminarily instructing a GAN model on an unmapped target data set to create images from an existing data set based on random vectors, constructing a decoder by displaying outputs of intermediate layers of GAN in a semantic segmentation mask, annotating with human involvement several images created by GAN, masks of semantic segmentation, teaching with teacher decoder on pairs of introduced features and corresponding annotated masks to produce pairs of synthetic images with corresponding pixel by annotations, creating synthetic data set, wherein synthetic data set consists of pairs of images with corresponding segmentation masks, by selecting random vector from normal distribution and supplying random noise to GAN input, which displays it in synthetic image, a separate semantic segmentation network is trained under supervision on the created synthetic data set.

EFFECT: technical result is higher efficiency of deep training algorithms.

6 cl, 2 tbl, 6 dwg