【摘要】 生成对抗网络(GAN)被广泛应用于图像生成。生成恒星和星系图像对预测未知恒星和星系有着重要的意义。首次将GAN用于生成天文图像,给出了天文图像生成的GAN模型结构;设计了GAN训练的策略;为了提高GAN的稳定性,提出了改进的神经元抛弃方法,通过网格搜索法对模型中的部分高级参数进行了优化,并采用了韦氏距离对损失函数进行了改进。以斯隆数字巡天数据库(SDSS)中的恒星以及星系图像作为训练图像,采用改进方法和原始GAN分别生成了2种不同分辨率的恒星和星系图像,并进行了对比,验证了改进方法的有效性。更多还原

【Abstract】 GAN technology has been widely used in image generation field. Generating images of stars and galaxy is of great significance for the prediction of unknown stars and galaxy. GAN has been used to generate star-galaxy images in this paper; the GAN model structure was built; the training strategy for GAN was designed; in order to stabilize the training procedure, we proposed a gird search method for the optimization of several hyper-parameters and an improved neuron discard method, EM-distance was used to modify the loss function in original GAN model. Taking the star-galaxy images in the Sloan digital sky survey(SDSS) as the training dataset, the improved method proposed in this paper and the original GAN were respectively used to generate two kinds of stars and galaxy images with different resolutions, and the comparison has been made to verify the effectiveness of the improved method.更多还原