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 3011–3020 of 4856 papers

Title	Date	Tasks	Status
Look at Adjacent Frames: Video Anomaly Detection without Offline Training	Jul 27, 2022	Anomaly DetectionVideo Anomaly Detection	—Unverified
Task Agnostic and Post-hoc Unseen Distribution Detection	Jul 26, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety	Jul 26, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	Jul 25, 2022	Anomaly DetectionTime Series	—Unverified
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection	Jul 24, 2022	Anomaly DetectionContrastive Learning	CodeCode Available
e-G2C: A 0.14-to-8.31 μJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM	Jul 24, 2022	Anomaly Detection	—Unverified
A general-purpose method for applying Explainable AI for Anomaly Detection	Jul 23, 2022	Anomaly DetectionExplainable Artificial Intelligence (XAI)	—Unverified
Anomaly Detection for Fraud in Cryptocurrency Time Series	Jul 23, 2022	Anomaly DetectionTime Series	—Unverified
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network	Jul 21, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images	Jul 20, 2022	Anomaly DetectionDiagnostic	—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