PaDiM
A memory-bank-based industrial anomaly detection method that uses a pretrained feature extractor to build a feature distribution of normal samples and performs anomaly detection via the Mahalanobis distance.
Timeline (last 90 days)
Memory bank methods (such as PatchCore and PaDiM) store a set of normal sample features during training and determine anomalies at inference via nearest-neighbor retrieval, with storage cost growing linearly with the training set size.
PaDiM and PatchCore are anomaly detection algorithms trained solely on normal samples and do not require defect samples to function.
All Facts (2)
Memory bank methods (such as PatchCore and PaDiM) store a set of normal sample features during training and determine anomalies at inference via nearest-neighbor retrieval, with storage cost growing linearly with the training set size.
50%UnverifiedPaDiM and PatchCore are anomaly detection algorithms trained solely on normal samples and do not require defect samples to function.
50%