The 2020 Data Science Dictionary--Key Terms You Need to Know

GANs–Generative adversarial networks (GANs) are deep neural network architectures comprised of two nets pitting one against the other, e.g. the term "adversarial"). The theory of GANs was first introduced in a 2014 paper by deep learning luminary Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio. The potential of GANs is significant because they are generative models in that they create new data instances that resemble training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person.