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 4261–4270 of 4856 papers

Title	Date	Tasks	Status
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text	Jul 1, 2019	Anomaly DetectionContextual Anomaly Detection	CodeCode Available
Anomaly Subsequence Detection with Dynamic Local Density for Time Series	Jun 28, 2019	Anomaly DetectionDensity Estimation	—Unverified
An Improvement of PAA on Trend-Based Approximation for Time Series	Jun 28, 2019	Anomaly DetectionDimensionality Reduction	—Unverified
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data	Jun 28, 2019	Anomaly DetectionMulti-Task Learning	—Unverified
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty	Jun 28, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Detection and Statistical Modeling of Birth-Death Anomaly	Jun 27, 2019	Anomaly Detection	—Unverified
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle	Jun 27, 2019	Anomaly DetectionIntrusion Detection	—Unverified
A Survey on GANs for Anomaly Detection	Jun 27, 2019	Anomaly DetectionSurvey	CodeCode Available
AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN	Jun 24, 2019	Anomaly DetectionEdge Detection	—Unverified
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network	Jun 24, 2019	Anomaly DetectionTime Series	—Unverified

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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