EAC-GAN: Semi-supervised Image Enhancement Technology to Improve CNN Classification Performance

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AbstractDeep neural networks require a large amount of data for supervised training and learning, but it is often difficult to obtain a large amount of label data in practical applications. Since semi-supervised learning can reduce the dependence of deep networks on label data, the generative confrontation network based on semi-supervised learning can improve the classification effect. Although researchers have made progress in semi-supervised learning, small-scale, fully-supervised tasks have not been solved well, because even unlabeled data cannot be obtained in large quantities in such tasks. Therefore, we propose a new GAN model EAC-GAN based on the auxiliary classifier generative adversarial network (ACGAN), which uses genetic algorithms and semi-supervised algorithms to improve classification under fully supervised conditions. Our method utilizes ACGAN to generate artificial data for supplementing supervised classification. More specifically, we attached an external classifier to the original ACGAN generator, so it was named EAC-GAN instead of sharing an architecture with the discriminator. Our experiments show that the performance of EAC-GAN is far superior to standard methods based on data augmentation and regularization, and it is effective on a small amount of real data sets.KeywordsImage synthesisData enhancementGenerative adversarial networkSemi-supervised learning