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 211–220 of 4856 papers

Title	Date	Tasks	Status	Hype
Do Language Models Understand Time?	Dec 18, 2024	Action RecognitionAnomaly Detection	CodeCode Available	1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning	Dec 17, 2024	Anomaly Detection	CodeCode Available	1
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization	Dec 16, 2024	Anomaly Detection	CodeCode Available	1
AD-LLM: Benchmarking Large Language Models for Anomaly Detection	Dec 15, 2024	Anomaly DetectionBenchmarking	CodeCode Available	1
Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection	Dec 13, 2024	Anomaly Detection	CodeCode Available	1
Federated Foundation Models on Heterogeneous Time Series	Dec 12, 2024	Anomaly DetectionFederated Learning	CodeCode Available	1
Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model	Dec 12, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Unlocking the Potential of Reverse Distillation for Anomaly Detection	Dec 10, 2024	Anomaly DetectionDecoder	CodeCode Available	1
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving	Dec 6, 2024	Anomaly DetectionAutonomous Driving	CodeCode Available	1
NLP-ADBench: NLP Anomaly Detection Benchmark	Dec 6, 2024	Anomaly DetectionFraud Detection	CodeCode Available	1

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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