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 711–720 of 4856 papers

Title	Date	Tasks	Status	Hype
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays	Jun 25, 2022	AnatomyAnomaly Detection	CodeCode Available	1
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection	Dec 23, 2024	Anomaly DetectionPrompt Learning	CodeCode Available	1
ADGym: Design Choices for Deep Anomaly Detection	Sep 27, 2023	Anomaly DetectionCloud Computing	CodeCode Available	1
LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection	Mar 14, 2024	Anomaly DetectionGraph structure learning	CodeCode Available	1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction	Aug 16, 2021	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1
Anomaly Detection in Dynamic Graphs via Transformer	Jun 18, 2021	Anomaly Detection	CodeCode Available	1
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial Buildings	Mar 30, 2022	Anomaly DetectionTime Series	CodeCode Available	1
Learning and Evaluating Representations for Deep One-class Classification	Nov 4, 2020	Anomaly DetectionClassification	CodeCode Available	1
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos	Sep 16, 2022	Anomaly DetectionObject	CodeCode Available	1
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots	Sep 20, 2022	Anomaly 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