Topics for Further Reading
Can’t get enough of GANs? Whether you’re still getting acquainted with foundational concepts, trying to keep up in a quickly moving field, or just looking for fun applications, we’ve put together some selected resources with a little something for everyone.
What are GANs and how do they work?
Evaluating GAN performance
What do we know about GANs, and what have we yet to discover?
How about some non-visual examples?
- PassGAN: A Deep Learning Approach for Password Guessing
- SSGAN: Secure Steganography Based on Generative Adversarial Networks
Just for fun
Select one of the images below to play games with GANs!
GANBREEDER create fantastic images
EDGES2... cats, shoes, buildings and more from line drawings
THIS PERSON DOES NOT EXISTso many faces - and none of them are real
GANPAINTactivate and deactivate neurons to make art
A brief history of GAN development
The original GAN
Directing generation with labeled data
Improvements to the original GAN architecture make DCGAN the new baseline
Encoding meaningful features to make GANs interpretable
Methods for improving training stability and encouraging convergence
Earth Mover loss function stabilizes training and prevents mode collapse
Adding layers as training progresses enables modeling of increasingly fine details
Incorporating attention improves image generation quality
Sometimes bigger really is better
Upgrading progressive generation with feature control
Lipschitz constraint guarantees the existence and uniqueness of the optimal discriminative function