Generative Adversarial Networks for Astronomical Images Generation
This work provides a tool for generating synthetic astronomical images, which could aid in data augmentation or visualization for astronomy enthusiasts, but it is incremental as it applies an existing GAN method to a new domain.
The paper tackled generating new astronomical images using a Lightweight GAN trained on web-sourced and Galaxy Zoo datasets, resulting in thousands of generated images of celestial bodies and galaxies, with code and images publicly available.
Space exploration has always been a source of inspiration for humankind, and thanks to modern telescopes, it is now possible to observe celestial bodies far away from us. With a growing number of real and imaginary images of space available on the web and exploiting modern deep Learning architectures such as Generative Adversarial Networks, it is now possible to generate new representations of space. In this research, using a Lightweight GAN, a dataset of images obtained from the web, and the Galaxy Zoo Dataset, we have generated thousands of new images of celestial bodies, galaxies, and finally, by combining them, a wide view of the universe. The code for reproducing our results is publicly available at https://github.com/davide-coccomini/GAN-Universe, and the generated images can be explored at https://davide-coccomini.github.io/GAN-Universe/.