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 671–680 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Attention-based residual autoencoder for video anomaly detection	May 25, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1	5
Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection	Feb 22, 2024	Anomaly DetectionDynamic Time Warping	CodeCode Available	1	5
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series	Apr 17, 2024	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization	Apr 8, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1	5
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models	May 31, 2023	Anomaly DetectionImage Reconstruction	CodeCode Available	1	5
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing	Sep 5, 2023	Anomaly DetectionTime Series	CodeCode Available	1	5
Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection	Jan 9, 2024	Anomaly DetectionDenoising	CodeCode Available	1	5
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRI	Sep 29, 2024	Anomaly DetectionDenoising	CodeCode Available	1	5
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection	Aug 23, 2021	Anomaly DetectionAttribute	CodeCode Available	1	5
Graph Neural Networks based Log Anomaly Detection and Explanation	Jul 2, 2023	Anomaly DetectionGraph Neural Network	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