Exploring Other Neural Network Architectures

6. Generative Adversarial Networks Part 2

Let’s explore some applications of GANs.

One of the major applications of GANs would be their ability to create new images. But why is this useful?

GANs play a crucial role in super-resolution tasks, where the goal is to enhance the resolution of images beyond their original quality.

This application is valuable in medical imaging, satellite imaging, and surveillance, where detailed information is essential. GANs can generate high-resolution images from lower-resolution inputs, offering a solution to challenges associated with limited hardware capabilities or image acquisition constraints.

Here are some examples of GANs in action!

Another application of GANs would be the creation of synthetic data. Similar to their application in super-resolution tasks where GANs can supplement low-quality data, GANs can be used in cases where the problem isn’t that the data is low quality, but that there isn’t any at all!

In astrophysics, for example, GAN’s have been used to replicate images resembling the Hubble Space Telescope eXtreme Deep Field (XDF). In this specific study (https://ui.adsabs.harvard.edu/link_gateway/2019MNRAS.490.4985S/doi:10.1093/mnras/stz2886), the generated images displayed a high level of fidelity with real XDF galaxies.

This application of a GAN shows the potential for GANs to generate useful and, most importantly, realistic mock surveys and synthetic data on a large scale, with potential applications in astrophysics and other fields.

GANs at work in astrophysics!