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 2161–2170 of 4856 papers

Title	Date	Tasks	Status	Hype
Topological Pooling on Graphs	Mar 25, 2023	Anomaly DetectionGraph Classification	CodeCode Available	0
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies	Mar 25, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available	2
Interpretable Anomaly Detection via Discrete Optimization	Mar 24, 2023	Anomaly DetectionDecision Making	—Unverified	0
Anomaly Detection under Distribution Shift	Mar 24, 2023	Anomaly Detection	CodeCode Available	1
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks	Mar 23, 2023	Anomaly DetectionFederated Learning	—Unverified	0
Confidence-Aware and Self-Supervised Image Anomaly Localisation	Mar 23, 2023	Anomaly DetectionMedical Image Analysis	CodeCode Available	0
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection	Mar 23, 2023	3D Anomaly Detection and SegmentationAnomaly Detection	CodeCode Available	1
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection	Mar 23, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning	Mar 22, 2023	Anomaly Detection	CodeCode Available	1
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection	Mar 22, 2023	Anomaly DetectionMultiple Instance Learning	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