GANs — An engine of lies

An example of AI generated human portraits using StyleGANs.
An ancient image recreated using GANs

How does it do this?

The magic lies in the math. The GAN essentially represents images in a latent space. A latent space can be thought of as a graph where every image it has seen before is represented as a dot. When a generator represents a picture of a car in a graph, it doesn’t mean it picks a random dot in the graph. It will pick a dot that falls in the nearby region of other car pictures. Thus creating a distribution of probabilities of an image being a car.

An example of a latent space
This image of a car is created completely using GANs
A representation of a GAN may learn patterns and generate images on minimal prompts.
Capabilities of GANs through the years



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Shaunak Inamdar

Shaunak Inamdar

Shaunak Inamdar is a CS undergrad with a passion for writing about technologies and making them accessible to a broader audience.