Master Assignment
[B] Unsupervised Anomaly Detection Under Domain Shifts
Type: Master EE/CS/ITC
Period: TBD
Student: (Unassigned)
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Background:
Unsupervised Anomaly Detection Under Domain Shifts
Unsupervised Anomaly Detection is usually based on pre-trained networks trained on benchmarking datasets such as ImageNet. However, a domain shift exists between these datasets and real-world data such as industrial or medical imaging. Shifting to the target domain using contrastive-learning inspired training has been proposed in ReContrast. This thesis will examine models like ReContrast for artificially introduced anomalies, domain shifts, and real-world medical image datasets, including from challenges such as the MICCAI 2023 OOD challenge.
- ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
- https://arxiv.org/abs/2306.02602
- https://github.com/guojiajeremy/recontrast
- Disentangling physical parameters for anomalous sound detection under domain shifts https://eurasip.org/Proceedings/Eusipco/Eusipco2022/pdfs/0000279.pdf