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 4601–4610 of 4856 papers

Title	Date	Tasks	Status
A Weighted Mutual k-Nearest Neighbour for Classification Mining	May 14, 2020	Anomaly DetectionClassification	CodeCode Available
VeeAlign: Multifaceted Context Representation using Dual Attention for Ontology Alignment	Feb 8, 2021	Anomaly DetectionData Integration	CodeCode Available
Anomaly Detection in Video Sequence with Appearance-Motion Correspondence	Aug 17, 2019	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection	Oct 13, 2022	Anomaly DetectionBenchmarking	CodeCode Available
Semi-supervised Anomaly Detection on Attributed Graphs	Feb 27, 2020	Anomaly DetectionAttribute	CodeCode Available
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets	May 7, 2025	Anomaly DetectionBinary Classification	CodeCode Available
AVID: Adversarial Visual Irregularity Detection	May 24, 2018	Anomaly Detection	CodeCode Available
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark	Jun 23, 2022	Anomaly Detectionimage-classification	CodeCode Available
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals	May 11, 2024	Anomaly DetectionCausal Inference	CodeCode Available
OptIForest: Optimal Isolation Forest for Anomaly Detection	Jun 22, 2023	Anomaly DetectionBenchmarking	CodeCode Available

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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