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Research questionHow can frozen-encoder anomaly detection for mechanical systems distinguish transferred representations from initialization and calibration artifacts?Strong anomaly separation can persist when embeddings are near zero or a feature stack was not actually loaded, so scores alone do not establish representation transfer. Calibration choices and evaluation-time definitions can also change apparent warning performance relative to classical baselines.
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Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol EffectsThe audit examines a preserved EfficientNet-B0 checkpoint and embeddings, controlled IMS evaluations, and PRONOSTIA fixed-time evaluation. It reports reproducible discrimination but finds that checkpoint loading, near-zero representations, Mahalanobis scoring, and lifetime-fraction evaluation can affect attribution; its conclusions are limited to the preserved artifacts and examined datasets.research paper · Sep 1, 2026
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