Höllig, JacquelineGrimm, FlorianKiefer, DanielThoma, SteffenKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-3https://dl.gi.de/handle/20.500.12116/45120Detection of defective parts and tools is essential in large-scale industrial manufacturing, playing a vital role in predictive maintenance, quality assurance, and safety hazard minimization. While traditionally performed by humans, the automation of visual anomaly detection using neural networks has gained prominence due to their increasing performance capabilities. However, deep learning models require extensive data for training, while acquiring annotated data is both costly and labor-intensive, especially for defect variations in industrial scenarios. Unsupervised methods, trained without labels or annotations, offer a potential solution but struggle to distinguish true anomalies from irrelevant impurities. To address the limitations of data dependency and spurious correlations in deep learning models, we introduce a demonstrator utilizing Human Importance-aware Network Tuning (HINT) to incorporate domain knowledge during training, and Explainable Artificial Intelligence (XAI) to provide insights into the model’s decision-making process.enIndustrial Anomaly DetectionExplainable Interactive Machine LearningHuman-in-the-LoopText/Conference Paper10.18420/inf2024_1451617-5468