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 2651–2660 of 4856 papers

Title	Date	Tasks	Status
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection	Mar 23, 2023	Anomaly DetectionContrastive Learning	—Unverified
Confidence-Aware and Self-Supervised Image Anomaly Localisation	Mar 23, 2023	Anomaly DetectionMedical Image Analysis	CodeCode Available
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks	Mar 23, 2023	Anomaly DetectionFederated Learning	—Unverified
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model	Mar 22, 2023	Anomaly DetectionTime Series	—Unverified
Defect Detection Approaches Based on Simulated Reference Image	Mar 21, 2023	Anomaly DetectionDefect Detection	—Unverified
A Novel Multi-Stage Approach for Hierarchical Intrusion Detection	Mar 21, 2023	Anomaly DetectionIntrusion Detection	CodeCode Available
Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation	Mar 21, 2023	Anomaly DetectionBinary Classification	—Unverified
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images	Mar 21, 2023	Anomaly DetectionChange Detection	CodeCode Available
Unlocking Layer-wise Relevance Propagation for Autoencoders	Mar 21, 2023	Anomaly DetectionMachine Translation	—Unverified
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking	Mar 20, 2023	Anomaly Detection	CodeCode Available

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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