20 Deployment, monitoring and human-in-the-loop for learning-based vision
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20.1 From trained model to production system
20.1.1 Model export and interchange (ONNX)
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20.1.2 Quantisation, pruning and operator fusion
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20.1.3 Runtime targets: CPU, GPU, edge accelerators
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20.1.4 Revalidation of the deployed artefact
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20.2 Dataset governance
20.2.1 Collection planning against the operating envelope
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20.2.2 Labelling protocols and inter-annotator agreement
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20.2.3 Curation, exclusion records and provenance
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20.2.4 Versioning and model-to-data traceability
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20.3 Post-deployment monitoring
20.3.1 Domain shift and concept drift
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20.3.2 Statistical drift detectors
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20.3.3 Retraining loops and change control
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20.4 Human-in-the-loop systems
20.4.1 Why humans remain in critical and deep-learning loops
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20.4.2 Review triggers: low confidence, drift, novelty, borderline cases
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20.4.3 Interface and workflow design for human review
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20.4.4 Human-in-the-loop as a labelling and improvement engine
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20.4.5 Levels of autonomy and human oversight
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20.4.6 Automation bias and the limits of human checking
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20.5 Explainability and interpretability
20.5.1 Saliency and Grad-CAM
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20.5.2 LIME and local surrogates
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20.5.3 SHAP and Shapley attributions
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20.5.4 Limits of post-hoc explanation
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20.5.5 Explanation as debugging versus explanation for audit
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20.6 Bias and fairness in industrial vision
20.6.1 Subgroups in industrial data: cavities, suppliers, shifts
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20.6.2 Subgroup analysis and reporting
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20.6.3 Mitigations: data, loss, imaging
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20.6.4 Fairness when the system observes people
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