OVERVIEW OF COMPUTER VISION SUPERVISED LEARNING TECHNIQUES FOR LOW-DATA TRAINING

Abstract

In the age of big data and machine learning the costs to turn the data into fuel for the algorithms is prohibitively high. Organizations that can train better models with fewer annotation efforts will have a competitive edge. This work is an overview of techniques of varying complexity and novelty for supervised, or rather weakly supervised learning for computer vision algorithms. The paper starts describing various methods to ease the need for a big labeled dataset with giving some background on supervised, weakly-supervised and then self-supervised learning in general, and in computer vision specifically. The paper describes the importance of these methods in fields such as medical imaging and autonomous driving.

Keywords

• knowledge distillation
• knowledge transfer
• self-supervised learning
• semi-supervised learning
• weakly-supervised learning