%0 Conference Paper %1 herde2023who %A Herde, Marek %A Huseljic, Denis %A Sick, Bernhard %A Bretschneider, Ulrich %A Oeste-Reiß, Sarah %B Workshop on Interactive Adapative Learning (IAL), ECML PKDD %D 2023 %K imported itegpub isac-www Crowdworker NoisyLabels ActiveLearning %P 14--18 %T Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning %U https://ceur-ws.org/Vol-3470/paper3.pdf %X Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.

@inproceedings{herde2023who, abstract = {Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.}, added-at = {2023-10-18T11:52:53.000+0200}, author = {Herde, Marek and Huseljic, Denis and Sick, Bernhard and Bretschneider, Ulrich and Oeste-Reiß, Sarah}, biburl = {https://www.bibsonomy.org/bibtex/2ddda1448b9e630fc1c900ccb5fdf24aa/ies}, booktitle = {Workshop on Interactive Adapative Learning (IAL), ECML PKDD}, codeurl = {https://github.com/ies-research/intelligent-crowdworker-selection}, interhash = {0415b4181f1b0906b897fe3b672a652f}, intrahash = {ddda1448b9e630fc1c900ccb5fdf24aa}, keywords = {imported itegpub isac-www Crowdworker NoisyLabels ActiveLearning}, pages = {14--18}, timestamp = {2023-10-18T11:52:53.000+0200}, title = {Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning}, url = {https://ceur-ws.org/Vol-3470/paper3.pdf}, year = 2023 }