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 2171–2180 of 4856 papers

Title	Date	Tasks	Status
Hyperbolic Anomaly Detection	Jan 1, 2024	Anomaly DetectionBenchmarking	—Unverified
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models	Dec 30, 2023	Anomaly DetectionInterpretable Machine Learning	—Unverified
Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone	Dec 28, 2023	Anomaly Detection	CodeCode Available
Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammograms	Dec 28, 2023	Anomaly DetectionFeature Correlation	CodeCode Available
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications	Dec 28, 2023	Anomaly DetectionFraud Detection	—Unverified
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	Dec 28, 2023	Anomaly DetectionDecision Making	—Unverified
Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics	Dec 27, 2023	Anomaly Detection	—Unverified
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features	Dec 27, 2023	Anomaly DetectionImage Reconstruction	—Unverified
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection	Dec 26, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Abnormal component analysis	Dec 26, 2023	Anomaly Detection	—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	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