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 2361–2370 of 4856 papers

Title	Date	Tasks	Status	Hype
Continual Learning Approaches for Anomaly Detection	Dec 21, 2022	Anomaly DetectionContinual Learning	CodeCode Available	0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series	Dec 21, 2022	Anomaly DetectionTime Series	CodeCode Available	0
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos	Dec 20, 2022	Anomaly DetectionSemantic Segmentation	—Unverified	0
Resonant Anomaly Detection with Multiple Reference Datasets	Dec 20, 2022	Anomaly Detection	—Unverified	0
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network	Dec 20, 2022	Action RecognitionAnomaly Detection	—Unverified	0
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks	Dec 20, 2022	Anomaly DetectionChange Detection	—Unverified	0
FedTADBench: Federated Time-Series Anomaly Detection Benchmark	Dec 19, 2022	Anomaly DetectionFederated Learning	CodeCode Available	1
CHAD: Charlotte Anomaly Dataset	Dec 19, 2022	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance	Dec 16, 2022	Anomaly DetectionClustering	—Unverified	0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection	Dec 15, 2022	Anomaly DetectionIntrusion Detection	—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	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