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 841–850 of 4856 papers

Title	Date	Tasks	Status	Hype
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics	Sep 24, 2024	Anomaly Detection	—Unverified	0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications	Sep 24, 2024	Anomaly DetectionFederated Learning	—Unverified	0
VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection	Sep 24, 2024	Anomaly DetectionDecoder	CodeCode Available	1
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection	Sep 24, 2024	Anomaly Detection	—Unverified	0
Anomaly Detection from a Tensor Train Perspective	Sep 23, 2024	Anomaly DetectionData Compression	—Unverified	0
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge	Sep 23, 2024	Anomaly DetectionVariational Inference	CodeCode Available	0
MotifDisco: Motif Causal Discovery For Time Series Motifs	Sep 23, 2024	Anomaly DetectionCausal Discovery	—Unverified	0
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning	Sep 23, 2024	Anomaly DetectionClustering	—Unverified	0
LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder	Sep 22, 2024	Anomaly DetectionImage Generation	—Unverified	0
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding	Sep 22, 2024	Anomaly DetectionGPU	CodeCode Available	4

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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