Who Cited It

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

2019 · 1,730 citations · 1 from inside this corpus

Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton van den Hengel

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.

Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervise… (2019)Memorizing Normality to Detec…Adam: A Method for Stochastic Optimization (2014)Adam: A Method for Stochastic…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh… (2015)Batch Normalization: Accelera…Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh… (2024)Batch Normalization: Accelera…Auto-Encoding Variational Bayes (2013)Auto-Encoding Variational Bay…Anomaly detection (2009)Anomaly detectionAutomatic differentiation in PyTorch (2017)Automatic differentiation in …On Estimation of a Probability Density Function and Mode (1962)On Estimation of a Probabilit…Learning with Kernels (2001)Learning with KernelsSupport Vector Method for Novelty Detection (1999)Support Vector Method for Nov…Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (2005)Learning with Kernels: Suppor…Anomaly detection in crowded scenes (2010)Anomaly detection in crowded …Future Frame Prediction for Anomaly Detection - A New Baseline (2018)Future Frame Prediction for A…Anomaly Detection with Robust Deep Autoencoders (2017)Anomaly Detection with Robust…Abnormal Event Detection at 150 FPS in MATLAB (2013)Abnormal Event Detection at 1…Learning Temporal Regularity in Video Sequences (2016)Learning Temporal Regularity …Towards Total Recall in Industrial Anomaly Detection (2022)Towards Total Recall in Indus…
17 of 17 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
Adam: A Method for Stochastic Optimization201484,698
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…201524,404
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…202415,663
Auto-Encoding Variational Bayes201315,583
Anomaly detection200911,511
Automatic differentiation in PyTorch201711,099
On Estimation of a Probability Density Function and Mode196210,596
Learning with Kernels20019,631
Support Vector Method for Novelty Detection19992,067
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond20051,758
Anomaly detection in crowded scenes20101,535
Future Frame Prediction for Anomaly Detection - A New Baseline20181,518
Anomaly Detection with Robust Deep Autoencoders20171,472
Abnormal Event Detection at 150 FPS in MATLAB20131,459
Learning Temporal Regularity in Video Sequences20161,407

What cites it, inside the corpus

PaperYearCited
Towards Total Recall in Industrial Anomaly Detection20221,560

Links

DOI · OpenAlex record

Topics

Anomaly Detection Techniques and ApplicationsComputer Science
Network Security and Intrusion DetectionComputer 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.

  • supports7 author record(s) attached.
  • supports81 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 2026-09-04T03:58:53+00:00.

sha256 db1645b78a57e29a…