Abstract: Industrial environments pose distinctive challenges for anomaly detection, primarily stemming from the complexities associated with high dimensionality and the dynamic nature of data ...
Abstract: Knowledge distillation has emerged as a primary solution for anomaly detection, leveraging feature discrepancies between teacher–student (T–S) networks to locate anomalies. However, previous ...
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