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 1191–1200 of 4856 papers

Title	Date	Tasks	Status	Hype
A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection	May 15, 2024	Anomaly DetectionAnomaly Localization	—Unverified	0
Model-Free Unsupervised Anomaly Detection Framework in Multivariate Time-Series of Industrial Dynamical Systems	May 14, 2024	Anomaly DetectionIncremental Learning	—Unverified	0
Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis	May 14, 2024	Anomaly Detection	CodeCode Available	0
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection	May 13, 2024	Anomaly DetectionTime Series	CodeCode Available	0
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data	May 13, 2024	Anomaly DetectionChange Detection	—Unverified	0
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving	May 13, 2024	Anomaly DetectionAutonomous Driving	CodeCode Available	1
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models	May 13, 2024	Anomaly DetectionEdge Detection	CodeCode Available	1
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems	May 13, 2024	Anomaly DetectionDimensionality Reduction	CodeCode Available	0
Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity	May 11, 2024	Anomaly DetectionClustering	—Unverified	0
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor Signals	May 11, 2024	Anomaly DetectionCausal Inference	CodeCode Available	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