PSIG-GAN: A Parameterized Synthetic Image Generator Optimized via Non-Differentiable GAN

Abstract

Deep convolutional neural networks have been successfully deployed by large, well-funded teams, but their wider adoption is often limited by the cost and schedule ramifications of their requirement for massive amounts of labeled data. We address this problem through the use of a parameterized synthetic image generator. Our approach is particularly novel in that we have been able to fine tune the generator’s parameters through the use of a generative adversarial network. We describe our approach, and present results that demonstrate its potential benefits. We demonstrate the PSIG-GAN by creating images for training a DCNN to detect the existence and location of weeds in lawn grass.