A peer-to-peer crowdsourcing platform for the labeled datasets forming

Abstract

Training dataset forming is quite labor intensive and frequently is of high costs. Also the cost overheads depend on the policies of the service, which implements the crowdsourcing approach to data labeling. In this paper a new peer-to-peer data labeling platform concept is presented, as well as the framework of the decentralized labeling approach is described briefly. The architecture proposed allows to avoid the intermediary labeling service and to perform the crowdsourcing-based data labeling by the computational facilities of users involved. Besides, the additional consensus procedure improves the quality of the labeled data by means of the voting procedure.