GANs in Action

Preface

Jakub Langr

When I first discovered GANs in 2015, I instantly fell in love with the idea. It was the kind of self-criticizing machine learning (ML) system that I always missed in other parts of ML. Even as humans, we constantly generate possible plans and then discriminate that just naively running into a door is not the best idea. GANs really made sense to me—to get to the next level of AI, we should take advantage of automatically learned representations and a machine learning feedback loop. After all, data was expensive, and compute was getting cheap.

The other thing I loved about GANs—though this realization came later—was its growth curve. No other part of ML is so “new.” Most of computer vision was invented before 1998, whereas ...