论文标题
使用Chang'e-4的数据，用于神经风格转移的周期符合生成的对抗网络
Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
论文作者
论文摘要
生成的对抗网络（GAN）在计算机视觉中具有巨大的应用。然而，在太空科学和行星探索的背景下，大门可以进行重大进展。我们介绍了处理Mission Chang'e-4的行星数据的工具，并使用渲染图像中的自行车矛盾提出了神经风格转移的框架。这些实验是在Iris Lunar Rover的背景下进行的。
Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances. We introduce tools to handle planetary data from the mission Chang'E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.
