What Is GAN Machine Learning?
GAN is the technological structure that sits behind a realistic AI face maker and allows users to create anything they wish, using deep learning knowledge to replicate real-life characteristics.
If you've tried making a person with This Person Does Not Exist, you'll have a good idea about how it works from one aspect, but perhaps not how the AI makes the magic happen. There are increasing uses for AI face generators, which means this smart, advanced tech is increasingly popular in digital artwork and creative media sectors.
What Does GAN Technology Do?
GAN is short for generative adversarial network, and it’s most often used to produce images, but capable of developing other outputs, including music. It uses deep learning to educate itself about how things appear, replicate key features, and generate something entirely original.
There are two networks within a GAN, called the generator and discriminator:
- The generator network builds new images or content.
- The discriminator challenges it and decides whether it thinks the content is of good enough quality.
- Rejected content is returned to the generator to refine until the discriminator is happy with the outcome.
As the GAN gains experience, it becomes more accurate and can make fast decisions about formulating an AI-generated face with the right characteristics that look life-like.
How Are GANs Used?
Aside from generating unique, digital human faces, GAN machine learning models are used across retail because they can understand, replicate or reimagine any form of visual content.
Examples include:
- Transforming an existing black and white image into full colour
- Generating realistic graphics to represent a prototype or idea
- Producing life-like graphics from text prompts
- Completing an image that only has an outline
- Modelling human behaviour patterns within set parameters
The two networks we mentioned work against data points collected within their database.
When the generator submits a piece of content, the discriminator shuffles through all the available information, delivering a probability score of between zero and one that the image is authentic. One means the graphic is real, and zero means it is 100% fake.
Only when the discriminator cannot tell or returns an acceptable score is the AI-generated image considered complete. In practical applications, artwork creators, designers and marketers can build original graphics very quickly and at minimal cost, without any concerns about photo distribution rights.
How Realistic Are GAN-Generated Human Faces?
A neural network learns from datasets–the more baseline data it has to study, the better the results will be. For example, if you provide a GAN with a dataset with millions of images of cats of all breeds, ages, colours, and sizes, it will be able to file away all the patterns and variances.
The network knows that most cats are fluffy, the features on their faces–nose, eyes, whiskers, mouth–and where they belong. It also knows that cats can be any one of a thousand colours with different lengths and textures of fur and tails. From there, the network breaks down those numerous components and individual features and mixes them up to create a digital cat that doesn't exist, and does not replicate any of the cats within the deep learning database.
This same concept applies to AI faces, which are so life-like that they are often impossible to distinguish from a genuine photo, yet display a portrait of an entirely digital person.