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 3001–3010 of 4856 papers

Title	Date	Tasks	Status
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data	Sep 5, 2022	Anomaly DetectionExplanation Generation	—Unverified
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries	Apr 24, 2024	Anomaly DetectionSelf-Supervised Learning	—Unverified
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning	Sep 29, 2021	Anomaly DetectionContrastive Learning	—Unverified
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation	Feb 4, 2021	Anomaly DetectionFederated Learning	—Unverified
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection	Feb 10, 2025	Anomaly DetectionIntrusion Detection	—Unverified
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series	Apr 19, 2021	Anomaly DetectionTime Series	—Unverified
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection	Jun 14, 2023	Anomaly DetectionData Augmentation	—Unverified
SALINA: Towards Sustainable Live Sonar Analytics in Wild Ecosystems	Oct 10, 2024	Anomaly Detectionenergy management	—Unverified
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection	Jun 2, 2024	Anomaly DetectionDefect Detection	—Unverified
Sampling High Throughput Data for Anomaly Detection of Data-Base Activity	Aug 14, 2017	Anomaly DetectionVocal Bursts Intensity Prediction	—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