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 2041–2050 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection with Inexact Labels	Sep 11, 2019	Anomaly DetectionMultiple Instance Learning	—Unverified
A geometric framework for outlier detection in high-dimensional data	Jul 1, 2022	Anomaly DetectionOutlier Detection	—Unverified
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network	Dec 23, 2021	Anomaly Detectionfeature selection	—Unverified
Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection	Oct 4, 2023	Anomaly Detectionwhole slide images	—Unverified
CRD: Collaborative Representation Distance for Practical Anomaly Detection	Dec 20, 2023	Anomaly DetectionComputational Efficiency	—Unverified
Anomaly Detection with HMM Gauge Likelihood Analysis	Jun 14, 2019	Anomaly DetectionClustering	—Unverified
CRC-SGAD: Conformal Risk Control for Supervised Graph Anomaly Detection	Apr 3, 2025	Anomaly DetectionGraph Anomaly Detection	—Unverified
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos	Mar 24, 2025	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Anomaly Detection with Generative Adversarial Networks	Jan 1, 2018	Anomaly Detection	—Unverified
Feature Extraction for Novelty Detection in Network Traffic	Jun 30, 2020	Anomaly DetectionBIG-bench Machine Learning	—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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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