关键词: 生成式对抗网络, 迁移学习, 目标样本, 字体生成

Abstract: In the confrontation training of the Generative Adversarial Networks（GAN）, insufficient training set of target samples will result in the model not being able to accurately learn the corresponding features, but it is difficult to obtain a target sample training set that needs to be manually produced and marked. Aiming at this problem, a two-layer GAN model based on migration learning is proposed. In the first layer network, the pseudo-target samples are used to let the model learn the approximate distribution of the target samples in the structure space, and the model migration is carried out by using the idea of migration learning, and is adjusted according to a small number of target samples in the second layer network. In experiment, the improvement of the model in Chinese font generation and picture frame graph conversion is verified, and a better model is effectively trained in a small number of target sample training sets.

Key words: Generative Adversarial Networks（GAN）, transfer learning, target sample, font generation