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 4501–4510 of 4856 papers

Title	Date	Tasks	Status
Null Hypothesis Test for Anomaly Detection	Oct 5, 2022	Anomaly Detection	CodeCode Available
Understanding the Effect of Bias in Deep Anomaly Detection	May 16, 2021	Anomaly Detection	CodeCode Available
From Zero to Hero: Cold-Start Anomaly Detection	May 30, 2024	Anomaly DetectionCold-Start Anomaly Detection	CodeCode Available
The role of data embedding in quantum autoencoders for improved anomaly detection	Sep 6, 2024	Anomaly Detection	CodeCode Available
Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling	Jan 17, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	CodeCode Available
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series	Jun 25, 2025	Anomaly DetectionBenchmarking	CodeCode Available
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video	Dec 11, 2018	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data	Jul 16, 2024	Anomaly DetectionDenoising	CodeCode Available
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems	May 13, 2024	Anomaly DetectionDimensionality Reduction	CodeCode Available
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain	Sep 2, 2016	Anomaly Detection	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