A novel quadruple generative adversarial network for semi-supervised categorization of low-resolution images

In order to make utilization of unlabeled low-resolution (LR) images to shape discriminative models, we present quadruple generative adversarial network (Q-GAN), a game-theoretical framework for implementing semi-supervised categorization of LR images. It can realize photo-realistic image super-resolution (SR) and semi-supervised pattern recognition simultaneously. We consider our pipeline as a four-player optimization-based formulation, which consists of four vital components, i.e., a refiner for image SR and generation, a discriminator for identifying high-resolution (HR) samples and another for identifying true (original) samples, a classifier for label prediction. The refiner and two discriminators characterize the conditional distributions between images and labels, whilst the classifier solely focuses on predicting real image-label pairs. We select those high-quality super-solved images with ground-truth labels for data supplement. We customize the global optimization objective function as well as the training procedure to ensure model approximates the posterior distribution of latent variables given true data in a semi-supervised manner. Experimental results demonstrate that Q-GAN can simultaneously (1) deliver the promising categorization performance among state-of-the-arts, i.e., validation accuracy achieves 92.18% and testing accuracy achieves 90.63%, and (2) recover fine-grained textures with high peak signalto-noise ratios (PNSRs) and structural similarities (SSIMs) from heavily downsampled testing images of hand-crafted dataset and public benchmarks. (C) 2020 Published by Elsevier B.V.