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 881–890 of 4856 papers

Title	Date	Tasks	Status	Hype
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation	Mar 18, 2020	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder	Mar 12, 2020	Anomaly DetectionQuantization	CodeCode Available	1
Isolation Mondrian Forest for Batch and Online Anomaly Detection	Mar 8, 2020	Anomaly DetectionEnsemble Learning	CodeCode Available	1
A^3: Activation Anomaly Analysis	Mar 3, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	1
MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach	Feb 27, 2020	Anomaly Detection	CodeCode Available	1
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks	Feb 22, 2020	Anomaly DetectionGeneral Classification	CodeCode Available	1
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection	Feb 21, 2020	Anomaly DetectionClustering	CodeCode Available	1
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection	Feb 18, 2020	Anomaly DetectionDecoder	CodeCode Available	1
Iterative energy-based projection on a normal data manifold for anomaly localization	Feb 10, 2020	Anomaly DetectionAnomaly Localization	CodeCode Available	1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks	Feb 10, 2020	Anomaly DetectionAttribute	CodeCode Available	1

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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