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 1101–1110 of 4856 papers

Title	Date	Tasks	Status	Hype
Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask	Jun 16, 2024	Anomaly Detection	CodeCode Available	1
Model Evaluation and Anomaly Detection in Temporal Complex Networks using Deep Learning Methods	Jun 15, 2024	Anomaly Detection	—Unverified	0
Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation	Jun 15, 2024	Anomaly DetectionOut-of-Distribution Generalization	CodeCode Available	0
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs	Jun 14, 2024	Anomaly DetectionBenchmarking	CodeCode Available	1
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry	Jun 14, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	0
Weakly-supervised anomaly detection for multimodal data distributions	Jun 13, 2024	Anomaly DetectionSupervised Anomaly Detection	—Unverified	0
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark	Jun 13, 2024	Anomaly DetectionARC	CodeCode Available	1
Few-Shot Anomaly Detection via Category-Agnostic Registration Learning	Jun 13, 2024	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Anomaly Detection Utilizing a Riemann Metric for Robust Myoelectric Pattern Recognition	Jun 12, 2024	Anomaly DetectionMotion Detection	—Unverified	0
Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical Aggregated Anomaly Detection	Jun 11, 2024	Anomaly Detection	—Unverified	0

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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