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 3641–3650 of 4856 papers

Title	Date	Tasks	Status
Isconna: Streaming Anomaly Detection with Frequency and Patterns	Apr 4, 2021	Anomaly Detection	CodeCode Available
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks	Apr 3, 2021	Anomaly DetectionComputational Efficiency	—Unverified
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies	Apr 2, 2021	Anomaly DetectionDynamic Time Warping	—Unverified
Detecting Anomalies Through Contrast in Heterogeneous Data	Apr 2, 2021	Anomaly DetectionContrastive Learning	—Unverified
The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection	Apr 1, 2021	Anomaly Detection	—Unverified
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection	Mar 31, 2021	Anomaly DetectionCPU	—Unverified
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning	Mar 31, 2021	Active LearningAnomaly Detection	—Unverified
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation	Mar 31, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Elsa: Energy-based learning for semi-supervised anomaly detection	Mar 29, 2021	Anomaly DetectionContrastive Learning	—Unverified
Anomaly Detection Under Multiplicative Noise Model Uncertainty	Mar 28, 2021	Anomaly Detectionmodel	—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	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