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 2621–2630 of 4856 papers

Title	Date	Tasks	Status
Few-shot Weakly-supervised Cybersecurity Anomaly Detection	Apr 15, 2023	Anomaly DetectionData Augmentation	—Unverified
Context-aware Domain Adaptation for Time Series Anomaly Detection	Apr 15, 2023	Anomaly DetectionDeep Reinforcement Learning	—Unverified
Signal Novelty Detection as an Intrinsic Reward for Robotics	Apr 14, 2023	AcrobotAnomaly Detection	CodeCode Available
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection	Apr 14, 2023	Anomaly DetectionDensity Estimation	—Unverified
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos	Apr 13, 2023	Anomaly Detection	—Unverified
Brain Structure Ages -- A new biomarker for multi-disease classification	Apr 13, 2023	Age EstimationAnatomy	—Unverified
Exploring Diffusion Models for Unsupervised Video Anomaly Detection	Apr 12, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph	Apr 11, 2023	Anomaly DetectionRepresentation Learning	—Unverified
Contrastive-Regularized U-Net for Video Anomaly Detection	Apr 11, 2023	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Video Event Restoration Based on Keyframes for Video Anomaly Detection	Apr 11, 2023	Anomaly DetectionVideo 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