What is Meta-Learning? An Introduction to the Setting and Algorithms


Abstract To date, the majority of the breakthroughs seen in machine learning have been in domains or settings where there was an abundance of data, either real or simulated. In contrast, the capability for humans to quickly recognise and discriminate between types of phenomena, for example in visual or acoustic settings, remains unmatched. Meta-learning, or learning to learn, has proven to be a popular and successful framework in dealing with these kind of few-shot problems. In this presentation I will give some general context to how these frameworks are created and used as well as some details on the different algorithms that have been a staple of the field.