Authors
Tobias Baur, Alexander Heimerl, Florian Lingenfelser, Johannes Wagner, Michel F Valstar, Björn Schuller, Elisabeth André
Publication date
2020/6
Journal
KI-Künstliche Intelligenz
Volume
34
Pages
143-164
Publisher
Springer Berlin Heidelberg
Description
In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool called NOVA. The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annotation process by giving the possibility to pre-label data automatically, resulting in a drastic acceleration of the annotation process. Furthermore, the user-interface implements recent eXplainable AI techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation …
Total citations
2020202120222023202429992
Scholar articles
T Baur, A Heimerl, F Lingenfelser, J Wagner… - KI-Künstliche Intelligenz, 2020