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 3721–3730 of 4856 papers

Title	Date	Tasks	Status	Hype
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features	Oct 15, 2020	Anomaly DetectionDecoder	—Unverified	0
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection	Oct 14, 2020	Anomaly DetectionTime Series	—Unverified	0
Detecting Anomalies from Video-Sequences: a Novel Descriptor	Oct 13, 2020	Anomaly Detection	—Unverified	0
Anomaly Detection With Conditional Variational Autoencoders	Oct 12, 2020	Anomaly Detection	—Unverified	0
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation	Oct 12, 2020	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate	Oct 10, 2020	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series	Oct 10, 2020	Anomaly DetectionTime Series	CodeCode Available	1
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation	Oct 10, 2020	Anomaly DetectionOutlier Detection	—Unverified	0
Algorithmic Frameworks for the Detection of High Density Anomalies	Oct 9, 2020	Anomaly DetectionVocal Bursts Intensity Prediction	CodeCode Available	0
Unsupervised 3D Brain Anomaly Detection	Oct 9, 2020	Anomaly Detection	—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	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