Abstract: The development of GANs (generative adversarial networks) has brought great progress to many traditional fields including image generation. By training GANs with a small number of samples, the features of specific image categories can be learned, and then the generated samples can be applied to a variety of tasks such as scene testing and training other networks. This work explores the automatic generation of Mars image samples from limited training datasets. A cooperative clustering training generating method for Mars sample generation is proposed based on progressive growing GANs, which alleviates the problems that the features of samples are not obvious, and the types of features are insufficient when directly adopting GANs to sample generation. Our results show that the generated samples are improved compared with the baseline original progressive growing GANs.

Key words: generative adversarial networks, image generation, data enhancement, mode collapse