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 2561–2570 of 4856 papers

Title	Date	Tasks	Status
Attentioned Convolutional LSTM InpaintingNetwork for Anomaly Detection in Videos	Nov 26, 2018	Anomaly DetectionCommon Sense Reasoning	—Unverified
Attention Fusion Reverse Distillation for Multi-Lighting Image Anomaly Detection	Jun 7, 2024	Anomaly Detection	—Unverified
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management	Feb 25, 2024	Anomaly DetectionData Augmentation	—Unverified
Attention Guided Anomaly Localization in Images	Nov 19, 2019	Anomaly DetectionAnomaly Localization	—Unverified
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection	Aug 14, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation	Mar 31, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study	Jan 13, 2024	Anomaly Detection	—Unverified
Attention to Patterns is all you need for Insider threat detection	Oct 26, 2024	AllAnomaly Detection	—Unverified
Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security	Mar 31, 2024	Anomaly Detectionobject-detection	—Unverified
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories	Sep 10, 2016	3D Action RecognitionAction Recognition	—Unverified

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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