GAN

• They can make entirely new image that are realistic, even they never been seen before
• Most of the application for GANs have been images

STACKGAN 

• Takes a textual description of the bird and than generating a high resolution of a bird matching that description.
• These pictures have never been seen before. It is not running a image search on a database, infact GAN is drawing a probability distribution over all hypothetical images matching that description
• We can keep running the GAN to get more images.

IGAN

• Developed in collaboration with adobe and Berkeley to help artist
• As the user draws a very crude sketches using the mouse,iGAN searches for the nearest possible realistic image.
• Ex scribble of green paint is turned into a lush grassland or a black triangle turned into a detailed mountain.

IMAGE TO IMAGE TRANSLATION

• Images in one domain can be turned into image in another domain.
• Blueprints for buildings can be turned into photos of finished buildings.
• Drawings of cat can be changed into a realistic photos of cats.
• Can be trained into unsupervised way.

CYCLEGAN

• Good at unsupervised image to image translation.
• Ex : Transforms the video of a horse to a video of a zebra.
• As the changes are completely un supervised. It changes the surroundings as zeebra live in a different environment. The background shown below looks more like a african background

Note:

• GANS are used for imitation learning ( ex imitate the action taken by a human expert)
• GANs are not limited to the visual domain