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 2961–2970 of 4856 papers

Title	Date	Tasks	Status
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification	Sep 9, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified
Anomaly Detection through Unsupervised Federated Learning	Sep 9, 2022	Anomaly DetectionFederated Learning	CodeCode Available
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques	Sep 8, 2022	Anomaly DetectionSentiment Analysis	—Unverified
Suspicious and Anomaly Detection	Sep 8, 2022	Anomaly Detection	—Unverified
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection	Sep 7, 2022	Anomaly DetectionRetrieval	CodeCode Available
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data	Sep 5, 2022	Anomaly DetectionExplanation Generation	—Unverified
ADTR: Anomaly Detection Transformer with Feature Reconstruction	Sep 5, 2022	Anomaly Detection	—Unverified
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography	Sep 5, 2022	Anomaly DetectionDimensionality Reduction	—Unverified
Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples	Sep 4, 2022	Anomaly Detection	—Unverified
Neuromorphic implementation of ECG anomaly detection using delay chains	Sep 2, 2022	Anomaly 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