ritterdennis/topex-printer

Tasks

• image-classification

Citation text from the card

@InProceedings{10.1007/978-3-031-74640-6_33,
author="Ritter, Dennis
and Hemberger, Mike
and H{\"o}nig, Marc
and Stopp, Volker
and Rodner, Erik
and Hildebrand, Kristian",
editor="Meo, Rosa
and Silvestri, Fabrizio",
title="CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification",
booktitle="Machine Learning and Principles and Practice of Knowledge Discovery in Databases",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="399--415",
abstract="In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting. In contrast to standard natural object benchmarks existing in the field, our results highlight the most important design choices when only category-labeled CAD models are available but classification needs to be done with real-world images. Our domain adaptation pipeline achieves SoTA performance on the VisDA benchmark, but more importantly, drastically improves recognition performance on our new open industrial dataset comprised of 102 mechanical parts. We conclude with a set of guidelines that are relevant for practitioners needing to apply state-of-the-art unsupervised domain adaptation in practice. Our code is available at https://github.com/dritter-bht/synthnet-transfer-learning.",
isbn="978-3-031-74640-6"
}

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