Abstract:

Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar, it is prone to produce mode collapse, resulting in poor image generation effect.A progressive growing of conditional generative adversarial networks (PGCGAN) model was proposed.The idea of conditional generative adversarial networks (CGAN) was introduced into PGGAN.Using category information as condition, PGGAN was improved in two aspects of network structure and mini-batch standard deviation, and the phenomenon of model collapse in the process of image generation was alleviated.In the experiments on the three data sets, compared with PGGAN, PGCGAN has a greater degree of improvement in inception score and Fréchet inception distance, two evaluation indicators for image generation, and the generated images have higher diversity and authenticity; and PGCGAN multiple unrelated datasets can be trained simultaneously without crashing, and high-quality images can be produced in datasets with imbalanced categories or data that are too similar and dissimilar.

Key words: generative adversarial network, progressive growing of conditional GAN, mini-batch standard deviation, image generation