Pith. sign in

REVIEW 2 cited by

Deep Industrial Image Anomaly Detection: A Survey

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 2301.11514 v5 pith:S7SUMVBD submitted 2023-01-27 cs.CV

Jiaqi Liu , Guoyang Xie , Jinbao Wang , Shangnian Li , Chengjie Wang , Feng Zheng , Yaochu Jin This is my paper
classification cs.CV
keywords anomalydetectionimagedeepindustrialarchitecturesnetworkresearch
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

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

  1. SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark

    cs.CV 2025-06 conditional novelty 7.0 of 10

    SiM3D provides a multiview, multimodal 3D anomaly detection benchmark with single-instance training and synthetic-to-real evaluation, showing adapted 2D methods often beat multimodal 3D methods on the new voxel-based task.

  2. Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutions

    cs.CV 2025-08 conditional novelty 5.0 of 10

    HierCore uses semantic clustering to build per-cluster memory banks, and the authors report stable anomaly detection performance across all four label-availability scenarios.

Pith tools