Training a generative adversarial network for performing semantic segmentation of images

Abstract

A method of training a generative adversarial network for performing semantic segmentation of images. The generative adversarial network includes a generator neural network and a critic neural network. The method includes using the generator neural network to generate predicted image segmentation maps from input images, wherein each predicted image segmentation map includes a classification prediction for each of a plurality of pixels of a respective input image, providing predicted image segmentation maps generated by the generator neural network to the critic neural network, training the critic neural network to determine weights for respective pixels of a predicted image segmentation map generated by the generator neural network, wherein the weight for each pixel is used to weight a pixel-wise cross entropy term in an objective function for the critic neural network, and using the weights determined by the critic neural network to train the generator neural network.