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 991–1000 of 4856 papers

Title	Date	Tasks	Status
Challenges for Unsupervised Anomaly Detection in Particle Physics	Oct 13, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection	Nov 27, 2021	Anomaly DetectionDecoder	—Unverified
Anomaly Detection for Network Connection Logs	Dec 1, 2018	Anomaly DetectionClustering	—Unverified
Background subtraction on depth videos with convolutional neural networks	Jan 17, 2019	Anomaly DetectionObject Tracking	—Unverified
Back Home: A Machine Learning Approach to Seashell Classification and Ecosystem Restoration	Jan 8, 2025	Anomaly Detectionvalid	—Unverified
Automated Anomaly Detection on European XFEL Klystrons	May 20, 2024	Anomaly DetectionDimensionality Reduction	—Unverified
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework	Oct 1, 2024	Anomaly Detection	—Unverified
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems	Dec 9, 2020	Anomaly DetectionGenerative Adversarial Network	—Unverified
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection	Dec 17, 2024	Anomaly DetectionBackdoor Attack	—Unverified
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder	Jan 11, 2021	Anomaly Detection	—Unverified

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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