User:Aitantv/GAN: Difference between revisions

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* First introduced in 2014  
* First introduced in 2014  
* [https://thispersondoesnotexist.com thispersondoesnotexist.com] created by nvidia
* [https://thispersondoesnotexist.com 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.
* 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
Deep Learning Software

Revision as of 22:46, 7 December 2021

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. Neither side of the equation should have the upper hand.

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

Playing with Latent Space