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 121–130 of 4856 papers

Title	Date	Tasks	Status	Hype
VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection	Aug 22, 2023	Anomaly DetectionBinary Classification	CodeCode Available	2
Generative AI for Medical Imaging: extending the MONAI Framework	Jul 27, 2023	Anomaly DetectionDenoising	CodeCode Available	2
Solving Data Quality Problems with Desbordante: a Demo	Jul 27, 2023	Anomaly DetectionDescriptive	CodeCode Available	2
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection	Jul 7, 2023	Anomaly DetectionImputation	CodeCode Available	2
FITS: Modeling Time Series with 10k Parameters	Jul 6, 2023	Anomaly DetectionTime Series	CodeCode Available	2
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection	Jun 17, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	2
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects	Jun 16, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	2
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection	Jun 15, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	2
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series	May 30, 2023	Anomaly DetectionClassification	CodeCode Available	2
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD	May 27, 2023	Anomaly ClassificationAnomaly Detection	CodeCode Available	2

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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