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 611–620 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots	Aug 26, 2020	Anomaly DetectionSegmentation	CodeCode Available	1	5
Hybrid Open-set Segmentation with Synthetic Negative Data	Jan 19, 2023	Anomaly DetectionSegmentation	CodeCode Available	1	5
Identify Backdoored Model in Federated Learning via Individual Unlearning	Nov 1, 2024	Anomaly DetectionFederated Learning	CodeCode Available	1	5
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images	Feb 18, 2023	Alzheimer's Disease DetectionAnomaly Detection	CodeCode Available	1	5
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection	Jul 25, 2022	Anomaly DetectionOne-Class Classification	CodeCode Available	1	5
Anomaly Detection Requires Better Representations	Oct 19, 2022	3D Anomaly Detection and SegmentationAnomaly Detection	CodeCode Available	1	5
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection	Jul 19, 2023	Anomaly DetectionDenoising	CodeCode Available	1	5
Building an Automated and Self-Aware Anomaly Detection System	Nov 10, 2020	Anomaly DetectionTime Series	CodeCode Available	1	5
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting	Dec 22, 2023	Anomaly DetectionTime Series	CodeCode Available	1	5
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection	Aug 19, 2022	Anomaly DetectionRepresentation Learning	CodeCode Available	1	5

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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