User:Aitantv/GAN: Difference between revisions
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* Deep Nostalgia | * Deep Nostalgia | ||
* Tokkingheads - combine a still with a video to make it talk | * Tokkingheads - combine a still with a video to make it talk | ||
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StyleGan2 | |||
* Create mega data set - at least 1000 images. You can use Fatkun plugin on chrome of DownThemAll to download all images on google. | |||
* 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. | |||
Revision as of 15:59, 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
StyleGan2
- Create mega data set - at least 1000 images. You can use Fatkun plugin on chrome of DownThemAll to download all images on google.
- 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.