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 3031–3040 of 4856 papers

Title	Date	Tasks	Status
Securing Your Transactions: Detecting Anomalous Patterns In XML Documents	Sep 9, 2012	Anomaly Detection	—Unverified
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers	Nov 4, 2024	Anomaly DetectionFeature Engineering	—Unverified
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs	Aug 21, 2020	Anomaly DetectionClassification	—Unverified
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection	Oct 28, 2020	Anomaly DetectionSemantic Segmentation	—Unverified
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection	Oct 29, 2020	Anomaly DetectionAutonomous Vehicles	—Unverified
Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots	Aug 26, 2022	Anomaly DetectionChange Detection	—Unverified
Self-Discriminative Modeling for Anomalous Graph Detection	Oct 10, 2023	Anomaly Detection	—Unverified
Self-Organising Maps in Computer Security	Aug 5, 2016	Anomaly DetectionComputer Security	—Unverified
Self-Supervised and Interpretable Anomaly Detection using Network Transformers	Feb 25, 2022	Anomaly Detection	—Unverified
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook	May 10, 2022	Anomaly DetectionSelf-Supervised 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