AI learns the essence of an image dataset to create believable invented photos [Top 100 journal articles of 2019]

This article is part 4 of a series reviewing selected papers from Altmetric's list of the top 100 most-discussed scholarly works of 2019. Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your 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. Because GANs can facilitate the creation of realistic images of people's faces that aren't actually real, they are potentially useful in knowledge management (KM) for the creation of avatars for personas1, chatbots, or gamification.