Learning GANs
A generative adversarial network (GAN) is a machine learning (ML) model in which two neural networks compete with each other to become more accurate in their predictions. GANs typically run unsupervised and use a cooperative zero-sum game framework to learn.
The two neural networks that make up a GAN are referred to as the generator and the discriminator. The generator is a convolutional neural network and the discriminator is a deconvolutional neural network. The goal of the generator is to artificially manufacture outputs that could easily be mistaken for real data. The goal of the discriminator is to identify which outputs it receives have been artificially created.
This Repo contains some COoL GANs like
- Image GANs
a) Fashion GAN
b) Handwritten Digits GAN
C) Cartoon GAN - Music GANs / with the use of magenta lib (https://magenta.tensorflow.org/)
- Style GANs
- Text to image (GPT-3 is an unsupervised learning algorithm using Generative Adversarial Network :)) )