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 2481–2490 of 4856 papers

Title	Date	Tasks	Status	Hype
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features	Oct 26, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	0
Detecting fake accounts through Generative Adversarial Network in online social media	Oct 25, 2022	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directions	Oct 25, 2022	Anomaly DetectionBenchmarking	—Unverified	0
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly Detection	Oct 25, 2022	Anomaly Detection	CodeCode Available	0
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings	Oct 25, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Bridging Machine Learning and Sciences: Opportunities and Challenges	Oct 24, 2022	Anomaly DetectionOut-of-Distribution Detection	—Unverified	0
Self-supervised Sparse Representation for Video Anomaly Detection	Oct 23, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation	Oct 23, 2022	Anomaly DetectionRepresentation Learning	—Unverified	0
Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data	Oct 21, 2022	Anomaly DetectionGPR	—Unverified	0
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance	Oct 20, 2022	Anomaly DetectionComputational Efficiency	—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