A Generative Adversarial Network, GAN in short, is a machine learning architecture where two neural networks compete against each other. One of them functions as a discriminator, seeking to optimize its classification of data (i.e., determine whether or not there is a cat in a picture). The other one functions as a generator, seeking to best generate new data to fool the discriminator (i.e., create realistic fake images of cats). Over time, the generator network will become increasingly good at simulating realistic data and being able to mimic real-life.
The concept of GAN was introduced by Ian Goodfellow in 2014, whom we know from the Machine Learning & Deep Learning book. Although GANs are computationally heavy and still undergoing major development, their potential implications are widespread. We can see these architectures taking over all sort of creative work, where generating new “data” is the main task. Think for instance of designing clothes, creating video footage, writing novels, animating movies, or even whole video games. One of my favorite Youtube channels discusses multiple of its recent applications, and here are a few of my favorites:
- App-building AI: pix2code
- Generating Images from Scratch: text2pix
- Image Synthesis from Text
- Image Editing with GAN
- AI learns to synthesize animal pictures
- AI makes drawing of your photo: pix2pix