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 3191–3200 of 4856 papers

Title	Date	Tasks	Status	Hype
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI	Sep 13, 2021	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection	Sep 13, 2021	Anomaly Detectionspeech-recognition	—Unverified	0
LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection	Sep 12, 2021	Anomaly Detection	—Unverified	0
A secondary immune response based on co-evolutive populations of agents for anomaly detection and characterization	Sep 11, 2021	Anomaly DetectionSpecificity	—Unverified	0
Towards a Rigorous Evaluation of Time-series Anomaly Detection	Sep 11, 2021	Anomaly DetectionTime Series	CodeCode Available	1
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams	Sep 10, 2021	Anomaly Detection	CodeCode Available	1
Enhancing Unsupervised Anomaly Detection with Score-Guided Network	Sep 10, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	0
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems	Sep 9, 2021	Anomaly Detection	—Unverified	0
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos	Sep 9, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	0
Detecting Attacks on IoT Devices using Featureless 1D-CNN	Sep 9, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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