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 671–680 of 4856 papers

Title	Date	Tasks	Status	Hype
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation	Dec 1, 2021	Anomaly DetectionGraph Neural Network	CodeCode Available	1
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection	Nov 26, 2021	AnatomyAnomaly Detection	CodeCode Available	1
SLA^2P: Self-supervised Anomaly Detection with Adversarial Perturbation	Nov 25, 2021	Anomaly DetectionPseudo Label	CodeCode Available	1
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes	Nov 24, 2021	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection	Nov 17, 2021	Anomaly DetectionOne-Class Classification	CodeCode Available	1
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection	Nov 16, 2021	Action RecognitionAnomaly Detection	CodeCode Available	1
Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection	Nov 15, 2021	Anomaly DetectionTime Series	CodeCode Available	1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows	Nov 15, 2021	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary	Oct 28, 2021	Anomaly DetectionData Augmentation	CodeCode Available	1
Sensing Anomalies as Potential Hazards: Datasets and Benchmarks	Oct 27, 2021	Anomaly Detection	CodeCode Available	1

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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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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