Uber Creates Generative Teaching Networks to Better Train Deep Neural Networks

A common analogy in artificial intelligence(AI) circles is that training data is the new oil for machine learning models. Just like the precious commodity, training data is scarce and hard to get at scale. While these type of models are relatively easy to create compare to other alternatives, they have a strong dependency in training data that results prohibited for most organizations. This problem becomes bigger with the scale of the machine learning models. Recently, Uber engineers published a paper proposing a new method called Generative Teaching Networks(GTNs) that create learning algorithms that automatically generate training data.