What Can We Learn from Small Data

Abstract

Over the past decade, deep learning has profoundly transformed the landscape of science and technology, from refining advertising algorithms to pioneering self-driving vehicles. While advancements in computational capabilities have fueled this evolution, the consistent availability of high quality training data is less of a given. In this work, the authors aim to provide a bird’s eye view on topics pertaining to small data scenarios, that is scenarios in which a less than desirable quality and quantity of data is given for supervised learning. We provide an overview for a set of challenges, proposed solution and at the end tie it together by practical guidelines on which techniques are useful in specific real-world scenarios.