User:Aitantv/GAN: Difference between revisions

Revision as of 14:51, 7 December 2021

Intro

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.

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

@@ Line 4: / Line 4: @@
 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.
-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 ==