Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1801–1825 of 4856 papers

Title	Date	Tasks	Status	Hype
SymED: Adaptive and Online Symbolic Representation of Data on the Edge	Sep 6, 2023	Anomaly DetectionData Compression	—Unverified	0
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source	Sep 5, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing	Sep 5, 2023	Anomaly DetectionTime Series	CodeCode Available	1
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability	Sep 5, 2023	Anomaly DetectionPrediction Intervals	—Unverified	0
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems	Sep 4, 2023	Anomaly DetectionDrift Detection	—Unverified	0
Towards frugal unsupervised detection of subtle abnormalities in medical imaging	Sep 4, 2023	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
Prior Knowledge Guided Network for Video Anomaly Detection	Sep 4, 2023	Anomaly DetectionKnowledge Distillation	—Unverified	0
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking	Sep 4, 2023	Anomaly DetectionAutoML	CodeCode Available	1
Are We Using Autoencoders in a Wrong Way?	Sep 4, 2023	Anomaly DetectionClassification	CodeCode Available	0
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection	Sep 3, 2023	Anomaly Detection	—Unverified	0
Anomaly detection with semi-supervised classification based on risk estimators	Sep 1, 2023	Anomaly DetectionOne-Class Classification	—Unverified	0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market	Aug 31, 2023	Anomaly DetectionFraud Detection	—Unverified	0
Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter	Aug 31, 2023	Anomaly Detection	—Unverified	0
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI	Aug 30, 2023	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Demo: A Digital Twin of the 5G Radio Access Network for Anomaly Detection Functionality	Aug 30, 2023	Anomaly Detection	—Unverified	0
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization	Aug 30, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Classification of Anomalies in Telecommunication Network KPI Time Series	Aug 30, 2023	Anomaly ClassificationAnomaly Detection	—Unverified	0
MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection	Aug 29, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models	Aug 29, 2023	Anomaly DetectionDenoising	—Unverified	0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification	Aug 29, 2023	Anomaly DetectionClassification	—Unverified	0
A Comprehensive Augmentation Framework for Anomaly Detection	Aug 29, 2023	Anomaly DetectionData Augmentation	—Unverified	0
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection	Aug 29, 2023	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	0
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models	Aug 29, 2023	Anomaly DetectionIn-Context Learning	CodeCode Available	3
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities	Aug 28, 2023	Anomaly DetectionData Augmentation	—Unverified	0
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks	Aug 28, 2023	Anomaly DetectionGraph Neural Network	CodeCode Available	1

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified