Label Efficient Learning Methods for Computer Vision Applications

Abstract

In this work, we review the challenges of data scarcity and label inefficiency in deep learning and survey efforts to overcome these challenges. Many label-efficient learning methods have been proposed, but there is still room to develop more effective methods. We introduce potential yet promising directions to achieve label-efficient learning in terms of data, learning methods, and efficient use of prior knowledge. We also present case studies involving the latest methods.