Heuristic Feedback for Generator Support in Generative Adversarial Network

The possibilities of using generative adversarial networks (GANs) are enormous due to the possibility of generating new data that can deceive the classifier. The zero-sum game between two networks is a solution used on an increasingly large scale in today’s world. In this paper, we focus on expanding the model of generative adversarial networks by introducing a block with a selected heuristic algorithm. The additional block allows for creating a set of features extracted from the discriminator. The heuristic algorithm is based on the analysis of feature maps and extracting the position of selected pixels. Then they are clustered into averaged sets of features and used on created images by the generator. If the specified number of points within any set of features is higher than the threshold value, then the generator performs classical training. Otherwise, the loss function is subject to the penalty function. The proposed mechanism affects the operation of the GAN through additional sample analysis concerning containing specific features. To analyze the solution and impact of the proposed heuristic feedback, tests were performed based on known data sets.