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GANs: Generative Adversarial Networks | GANs: Generative Adversarial Networks | ||
== 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 | |||
* | * | ||
Revision as of 13:45, 7 December 2021
Intro
GANs: Generative Adversarial Networks
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