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Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality

  • Conference paper
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Pattern Recognition (DAGM GCPR 2023)

Abstract

High-quality data is crucial for the success of machine learning, but labeling large datasets is often a time-consuming and costly process. While semi-supervised learning can help mitigate the need for labeled data, label quality remains an open issue due to ambiguity and disagreement among annotators. Thus, we use proposal-guided annotations as one option which leads to more consistency between annotators. However, proposing a label increases the probability of the annotators deciding in favor of this specific label. This introduces a bias which we can simulate and remove. We propose a new method CleverLabel for Cost-effective LabEling using Validated proposal-guidEd annotations and Repaired LABELs. CleverLabel can reduce labeling costs by up to 30.0%, while achieving a relative improvement in Kullback-Leibler divergence of up to 29.8% compared to the previous state-of-the-art on a multi-domain real-world image classification benchmark. CleverLabel offers a novel solution to the challenge of efficiently labeling large datasets while also improving the label quality.

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Acknowledgments

We acknowledge funding of L. Schmarje by the ARTEMIS project (Grant number 01EC1908E) funded by the Federal Ministry of Education and Research (BMBF, Germany). We further acknowledge the funding of J. Nazarenus by the OP der Zukunft project funded by the Business Development and Technlogy Transfer Corporation of Schleswig Holstein (WTSH, Germany) within the REACT-EU program. We also acknowledge the funding of M. Santarossa by the KI-SIGS project (grant number FKZ 01MK20012E) and the funding of V. Grossmann by the Marispace-X project (grant number 68GX21002E), both funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK, Germany).

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Schmarje, L. et al. (2024). Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality. In: Köthe, U., Rother, C. (eds) Pattern Recognition. DAGM GCPR 2023. Lecture Notes in Computer Science, vol 14264. Springer, Cham. https://doi.org/10.1007/978-3-031-54605-1_30

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