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 3421–3430 of 4856 papers

Title	Date	Tasks	Status
Universal Data Anomaly Detection via Inverse Generative Adversary Network	Jan 23, 2020	Anomaly Detection	—Unverified
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection	Apr 1, 2025	Adversarial AttackAdversarial Attack Detection	—Unverified
Unlocking Layer-wise Relevance Propagation for Autoencoders	Mar 21, 2023	Anomaly DetectionMachine Translation	—Unverified
Unlocking Multimodal Integration in EHRs: A Prompt Learning Framework for Language and Time Series Fusion	Feb 19, 2025	Anomaly DetectionPrompt Learning	—Unverified
Unmanned Aerial System Security using Real-time Autopilot Software Analysis	Jun 7, 2016	Anomaly Detection	—Unverified
Unmasking the abnormal events in video	May 23, 2017	Abnormal Event Detection In VideoAnomaly Detection	—Unverified
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them	Jan 22, 2024	Anomaly DetectionModel extraction	—Unverified
Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis	Jul 26, 2023	Anomaly DetectionObject Tracking	—Unverified
Unravelling physics beyond the standard model with classical and quantum anomaly detection	Jan 25, 2023	Anomaly Detection	—Unverified
Unseen Visual Anomaly Generation	Jan 1, 2025	Anomaly DetectionImage Generation	—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