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 1781–1790 of 4856 papers

Title	Date	Tasks	Status	Hype
Forensic Video Analytic Software	Sep 17, 2023	Activity RecognitionAnomaly Detection	—Unverified	0
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses	Sep 17, 2023	Anomaly DetectionTime Series	CodeCode Available	0
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style Transfer	Sep 16, 2023	Anomaly DetectionData Augmentation	CodeCode Available	0
Understanding the limitations of self-supervised learning for tabular anomaly detection	Sep 15, 2023	Anomaly DetectionSelf-Supervised Learning	—Unverified	0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection	Sep 14, 2023	Anomaly DetectionBinary Classification	—Unverified	0
An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone	Sep 14, 2023	Anomaly DetectionHyperparameter Optimization	—Unverified	0
Beta quantile regression for robust estimation of uncertainty in the presence of outliers	Sep 14, 2023	Anomaly DetectionImage Reconstruction	—Unverified	0
Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control	Sep 13, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection	Sep 13, 2023	Anomaly DetectionDefect Detection	CodeCode Available	1
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection	Sep 12, 2023	Anomaly Detection	—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