Image Classification Based on Principal Component Analysis optimized Generative Adversarial Networks

In this paper, a principal component analysis optimized generative adversarial networks (PCAGAN) is proposed. The original data is compressed and reduced by principal component analysis to generate the input of the confrontation network, so that the input data retains the characteristics of the original data to some extent, thereby improving the data generation performance and reducing the training time cost. We applied PCA-GAN to image classification, and the experimental results show that the model effectively improve the accuracy of classification and enhance the stability of the model.1