Who Cited It

Towards Total Recall in Industrial Anomaly Detection

2022 · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 1,560 citations · 0 from inside this corpus

Karsten Roth, Latha Pemula low, Joaquin Zepeda low, Bernhard Schölkopf, Thomas Brox, Peter Gehler

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Towards Total Recall in Industrial Anomaly Detection (2022)Towards Total Recall in Indus…Distilling the Knowledge in a Neural Network (2015)Distilling the Knowledge in a…Aggregated Residual Transformations for Deep Neural Networks (2017)Aggregated Residual Transform…Support Vector Data Description (2003)Support Vector Data Descripti…Support Vector Method for Novelty Detection (1999)Support Vector Method for Nov…MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection (2019)MVTec AD — A Comprehensive Re…Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervise… (2019)Memorizing Normality to Detec…Future Frame Prediction for Anomaly Detection - A New Baseline (2018)Future Frame Prediction for A…[No title in the source record — Newcastle University ePrints (Newcastle Univesity)][No title in the source recor…
8 of 8 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Distilling the Knowledge in a Neural Network201514,099
Aggregated Residual Transformations for Deep Neural Networks201712,015
Support Vector Data Description20033,526
Support Vector Method for Novelty Detection19992,067
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection20191,999
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervise…20191,730
Future Frame Prediction for Anomaly Detection - A New Baseline20181,518
[No title in the source record — Newcastle University ePrints (Newcastle Univesity)]1,494

Links

DOI · OpenAlex record

Topics

Anomaly Detection Techniques and ApplicationsComputer Science
Domain Adaptation and Few-Shot LearningComputer Science
Adversarial Robustness in Machine LearningComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports6 author record(s) attached.
  • supports98 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:57+00:00.

sha256 b3024609427ad450…