User:Aitantv/GAN

Intro

GANs: Generative Adversarial Networks

First introduced in 2014
thispersondoesnotexist.com created by nvidia
Generator v Discriminator: The generator tries to create random synthetic outputs (for instance, images of faces), while the discriminator tries to tell these apart from real outputs (say, a database of celebrities). The hope is that as the two networks face off, they'll both get better and better—with the end result being a generator network that produces realistic outputs. Neither side of the equation should have the upper hand.

Deep Learning Software

Generative Adversarial Networks (GAN)

thispersondoesnotexist.com
requires deep learning and neural network experience + coding experience
GAN made up of two neural networks- Generator + Discriminator
Introduced in 2013
Generator - creates data that is preceived to be real. It recieves input and generates realistic images based on those images.
Discriminator - decides which images created by the Generator are real and fake.
StyleGan / RunwayML (web software/app) very easy to use / BigBiGan

DeepFake

StyleGan2

Create mega data set - at least 1000 images. You can use Fatkun plugin on chrome of DownThemAll to download all images on google.
An algoithm can then auto-crop the data set so it's ready for the machine.
Through 'transfer learning' you can first train the machine using one data set (e.g. Umbrellas), then add a second data set (e.g. Clouds), and it will project the first onto the second.
Expect a final image/moving image of 512x512 or 1024x1024. You can always upscale to higher or lower resolutions

Voice

You can also teach a machine a voice. Once it knows, you can replicate using text.

Text to Images

Can create new bird species
Incredibly detailed hi res image generation
Shapeshifting
'Latent space'- the grey area where people dont really understand what's going on

Clips

Progressive Growing

Start with low res images & progresses to higher levels. Can take up to 10 days to get a convincing result.