The five “individuals” below have one thing in common: They are all the product of machine learning and data mining technologies brought together and introduced by computer scientists several years ago that are fully-operational today.
“Generative Adversarial Networks” provide a way for an individual or entity to create amazingly lifelike images of fictitious people upon which any identification and biography may be applied. Such graphics may then be used for any number of purposes–public relations/promotion and advertising, entertainment vehicles, or perhaps even controversial public events.
From the LyrnAI blog:
Generative Adversarial Networks (GAN) are a relatively new concept in Machine Learning, introduced for the first time in 2014. Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images. A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity faces. While GAN images became more realistic over time, one of their main challenges is controlling their output, i.e. changing specific features such pose, face shape and hair style in an image of a face.
A new paper by NVIDIA, A Style-Based Generator Architecture for GANs (StyleGAN), presents a novel model which addresses this challenge. StyleGAN generates the artificial image gradually, starting from a very low resolution and continuing to a high resolution (1024×1024). By modifying the input of each level separately, it controls the visual features that are expressed in that level, from coarse features (pose, face shape) to fine details (hair color), without affecting other levels.
This technique not only allows for a better understanding of the generated output, but also produces state-of-the-art results – high-res images that look more authentic than previously generated images.
You can demonstrate this technology and create your own cast of fictitious “persons” by visiting thispersondoesnotexist.com.