User:Aitantv/GAN: Difference between revisions
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[https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/ Machine Learning Mastery] | [https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/ Machine Learning Mastery] | ||
GANs: Generative Adversarial Networks | 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. | |||
== Method == | == Method == | ||
Revision as of 13:51, 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.
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