Collaborative Data Acquisition and Learning Support

Abstract

With the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an oracle and repository of training samples. The paper presents the CenHive system implementing the proposed approach. Three different usage scenarios are presented that were used to verify the proposed approach.