The Government of the United States of America, as represented by the Secretary of the Navy, Arlington, VA (US)
Abstract
An extended semi-supervised learning (ESSL) generative adversarial network (GAN) including metrics for evaluating training performance and a method for generating an estimated label vector y by the extended semi-supervised learning (ESSL) generative adversarial network (GAN) discriminator are described. Embodiments in accordance with the invention improve classification accuracy over convolutional neural networks with improved synthetic imagery
