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 2711–2720 of 4856 papers

Title	Date	Tasks	Status	Hype
Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange	Jun 26, 2022	Anomaly DetectionDecoder	—Unverified	0
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems	Jun 25, 2022	Anomaly DetectionData Poisoning	CodeCode Available	1
Multi Visual Modality Fall Detection Dataset	Jun 25, 2022	Anomaly Detection	CodeCode Available	1
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays	Jun 25, 2022	AnatomyAnomaly Detection	CodeCode Available	1
Self-Supervised Training with Autoencoders for Visual Anomaly Detection	Jun 23, 2022	Anomaly DetectionDenoising	—Unverified	0
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark	Jun 23, 2022	Anomaly Detectionimage-classification	CodeCode Available	0
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching	Jun 22, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs	Jun 21, 2022	Anomaly DetectionBenchmarking	CodeCode Available	0
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient	Jun 21, 2022	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	0
0/1 Deep Neural Networks via Block Coordinate Descent	Jun 19, 2022	10-shot image generation	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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