ActiveCrowds: A Human-in-the-Loop Machine Learning Framework

A widespread practice in machine learning solutions is the continuous use of human intelligence to increase their quality and efficiency. A common problem in such solutions is the requirement of a large amount of labeled data. In this paper, we present a practical implementation of the human-in-the-loop computing practice, which includes the combination of active and transfer learning for sophisticated data sampling and weight initialization respectively, and a cross-platform mobile application for crowdsourcing data annotation tasks. We study the use of the proposed framework to a post-event building reconnaissance scenario, where we utilized the implementation of an existing pre-trained computer vision model, an image binary classification solution built on top of it, and max entropy and random sampling as uncertainty sampling methods for the active learning step. Multiple annotations with majority voting as quality assurance are required for new human-annotated images to be added on the train set and retrain the model. We provide the results and discuss our next steps.