Pith. sign in

REVIEW 3 cited by

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2011.08785 v1 pith:EI7J4ECB submitted 2020-11-17 cs.CV

Thomas Defard , Aleksandr Setkov , Angelique Loesch , Romaric Audigier This is my paper · ORCID
classification cs.CV
keywords padimanomalylocalizationpatchanomaliesdetectiondistributionframework
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Procedurally generated water stains in synthetic training data reduce false alarms on real stains, and a sequential coreset variant of PatchCore makes high-resolution training memory-tractable.

  2. Wavelet-Enhanced PaDiM for Industrial Anomaly Detection

    cs.CV 2025-08 conditional novelty 5.0 of 10

    Replacing PaDiM's random channel sampling with per-layer wavelet subband selection yields test-set-optimized MVTec AD averages of 99.32% Image-AUC and 92.10% Pixel-AUC, and shows LL bands help detection while detail b...

  3. SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    SP-Mamba pairs a Mamba autoencoder with sliding-window anatomical prototypes and concentration/contrast anomaly scoring, reporting state-of-the-art unsupervised medical anomaly detection on three benchmarks.

Pith tools