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 2691–2700 of 4856 papers

Title	Date	Tasks	Status
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption	Mar 2, 2023	Anomaly DetectionDenoising	—Unverified
Multi-Task Self-Supervised Time-Series Representation Learning	Mar 2, 2023	Anomaly DetectionContrastive Learning	—Unverified
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series	Mar 2, 2023	Anomaly DetectionTime Series	CodeCode Available
CADeSH: Collaborative Anomaly Detection for Smart Homes	Mar 2, 2023	Anomaly DetectionIntrusion Detection	—Unverified
Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum	Mar 1, 2023	Anomaly Detection	—Unverified
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series	Mar 1, 2023	Anomaly DetectionTime Series	—Unverified
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails	Mar 1, 2023	Active LearningAnomaly Detection	—Unverified
Texture-Based Input Feature Selection for Action Recognition	Feb 28, 2023	Action RecognitionAnomaly Detection	—Unverified
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach	Feb 28, 2023	Anomaly DetectionScheduling	—Unverified
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism	Feb 27, 2023	Activity RecognitionAnomaly 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	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