User:Aitantv/GAN: Difference between revisions

Revision as of 22:42, 7 December 2021

GANs: Generative Adversarial Networks

First introduced in 2014
thispersondoesnotexist.com/ created by nvidia
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

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

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 GANs: Generative Adversarial Networks
-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.
+* First introduced in 2014
+* [https://thispersondoesnotexist.com thispersondoesnotexist.com]/ created by nvidia
+* 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