2021/10/13 by Lars Schmarje, Schmarje, Lars, Reinhard Koch +1
Computer Science · #Anomaly Detection Techniques and Applications #Machine Learning and Data Classification #Multimodal Machine Learning Applications #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2110.06592
Accepted at LWDA 21: Lernen, Wissen, Daten, Analysen September 2021, Munich, Germany
arxiv created 2021/10/13 · arxiv updated 2021/10/14
The required amount of labeled data is one of the biggest issues in deep learning. Semi-Supervised Learning can potentially solve this issue by using additional unlabeled data. However, many datasets suffer from variability in the annotations. The aggregated labels from these annotation are not consistent between different annotators and thus are considered fuzzy. These fuzzy labels are often not considered by Semi-Supervised Learning. This leads either to an inferior performance or to higher initial annotation costs in the complete machine learning development cycle. We envision the incorporation of fuzzy labels into Semi-Supervised Learning and give a proof-of-concept of the potential lower costs and higher consistency in the complete development cycle. As part of our concept, we discuss current limitations, futures research opportunities and potential broad impacts.