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 4081–4090 of 4856 papers

Title	Date	Tasks	Status
Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations	Oct 24, 2023	Anomaly Detection	CodeCode Available
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification	May 21, 2018	Anomaly DetectionClassification	CodeCode Available
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder	Jun 11, 2023	Anomaly DetectionContrastive Learning	CodeCode Available
Localizing Anomalies via Multiscale Score Matching Analysis	Jun 28, 2024	Anomaly DetectionAnomaly Localization	CodeCode Available
Statistical Evaluation of Anomaly Detectors for Sequences	Aug 13, 2020	Anomaly Detection	CodeCode Available
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention	Sep 28, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection	Aug 5, 2021	Anomaly DetectionData Augmentation	CodeCode Available
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	Jun 13, 2018	Anomaly DetectionDisease Prediction	CodeCode Available
Real-Time Anomaly Detection for Streaming Analytics	Jul 8, 2016	Anomaly DetectionTime Series	CodeCode Available
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series	Nov 21, 2024	Anomaly DetectionBenchmarking	CodeCode Available

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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