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 631–640 of 4856 papers

Title	Date	Tasks	Status	Hype
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection	Oct 26, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling	Nov 21, 2023	Anomaly DetectionTime Series	CodeCode Available	1
Explainable Deep One-Class Classification	Jul 3, 2020	Anomaly DetectionClassification	CodeCode Available	1
Explaining Anomalies Detected by Autoencoders Using SHAP	Mar 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	1
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization	Dec 16, 2024	Anomaly Detection	CodeCode Available	1
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection	Nov 5, 2023	Anomaly DetectionQuestion Answering	CodeCode Available	1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection	Mar 28, 2022	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1
Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark	Jan 11, 2025	Anomaly DetectionPose-based Anomaly Detection	CodeCode Available	1
Can Multimodal LLMs Perform Time Series Anomaly Detection?	Feb 25, 2025	Anomaly DetectionIrregular Time Series	CodeCode Available	1
Can LLMs Understand Time Series Anomalies?	Oct 7, 2024	Anomaly DetectionTime Series	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