User:Aitantv/GAN

Intro

Machine Learning Mastery

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.

Deep Learning Software

• pix2pix
• StyleGan

Method

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

• Deep Nostalgia
• Tokkingheads - combine a still with a video to make it talk

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.

StyleGan

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