Overview of computer vision supervised learning techniques for low-data training

Abstract

This work is an overview of techniques of varying complexity and novelty for supervised, or rather weakly supervised learning for computer vision algorithms. With the advent of deep learning the number of organizations and practitioners who think that they can solve problems using it also grows. Deep learning algorithms normally require vast amounts of labeled data, but depending on the domain it is not always possible to have a well annotated huge dataset, just think about healthcare.