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 3481–3490 of 4856 papers

Title	Date	Tasks	Status	Hype
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI	Mar 24, 2021	Anomaly DetectionLesion Detection	—Unverified	0
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability	Mar 24, 2021	Anomaly DetectionTime Series	—Unverified	0
Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise	Mar 24, 2021	Anomaly DetectionTime Series	CodeCode Available	0
Anomaly Detection using Principles of Human Perception	Mar 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients	Mar 22, 2021	Anomaly DetectionDeep Learning	CodeCode Available	0
Unsupervised Two-Stage Anomaly Detection	Mar 22, 2021	Anomaly DetectionImage Reconstruction	—Unverified	0
SSD: A Unified Framework for Self-Supervised Outlier Detection	Mar 22, 2021	Anomaly DetectionOutlier Detection	CodeCode Available	1
A Modular and Unified Framework for Detecting and Localizing Video Anomalies	Mar 21, 2021	Anomaly DetectionEvent Detection	—Unverified	0
Enhancing Robustness of On-line Learning Models on Highly Noisy Data	Mar 19, 2021	Anomaly DetectionFace Recognition	CodeCode Available	0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies	Mar 18, 2021	Anomaly DetectionGraph Learning	—Unverified	0

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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