关键词: 生成图像, 胶囊网络, 生成对抗网络, 卷积网络, 鲁棒性

Abstract:

In the implementation of the traditional text image confrontation model, the convolution network in the discriminator is used to extract image features, but the convolution network can not consider the spatial relationship between the underlying objects, resulting in poor quality of the generated image, and the capsule network is an effective method. In this paper, the traditional text conditional generation adversarial model is improved based on the capsule network method. The convolution network in the discriminator is replaced by the capsule network to enhance its robustness to image size. The experimental results on data set show that the new model can effectively improve the quality of generation. The FID value of flower image generation is reduced by 14.49%, and the FID value of bird image generation is reduced by 7.18%. The inception scores of the images generated on Oxford-102 and CUB data sets are 22.60% and 26.28% higher, which shows that the image features generated by the improved model are more rich and meaningful.

Key words: generating images, capsule network, generation adversarial network, convolutional network, robustness