All GAN models present here have been taken either from the book by Rowel Atienza "Advanced Deep Learning with Keras" or various web sources on Advanced GAN Models
- gan - Builder Script for Higher Models
- Vanilla GAN - Simple Convolutional GAN for mnist Data
- Deep Convolutional GAN - Deep Convolutional GAN
- Wasserstein GAN - Higher GAN which uses Wasserstein Loss Function
- Conditional GAN - Conditional GAN's where you can give an additional input of label and get the desired result
- Least Squares GAN - Higher GAN, more stable, Uses MSE loss
- Info GAN - Disentagled GAN,used to differentiate and generate between features in the generated images
- Stacked GAN - Hybridized Disentangled GAN,uses Enocders to build features that are used for generation
- Auxiliary Conditional GAN - Auxiliary Conditional GAN, same as cgan with I/O different
- Pix2Pix - Instance Based Cycle GAN, used for conditioning Noise(or other images) onto Specific Images
- Cycle GAN - Non mapping GAN, which can be used to learn representations between two sets of images, (~ Style Transfer)