Training a Machine Learning model from just a few examples: Few-Shot Learning — Part 2

Few-Shot Learning (FSL) is a field of Machine Learning that aims at training models in scenarios where very few examples are available as supervision. Using prior knowledge, FSL can master new tasks from a limited number of examples. This article series gives an introduction to various FSL approaches and is meant for beginner to intermediate level Machine Learning enthusiasts. This is Part 2 of the series and covers methods for achieving FSL. Part 1 focussed on a general introduction to the topic.