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 491–500 of 4856 papers

Title	Date	Tasks	Status	Hype
Can Multimodal LLMs Perform Time Series Anomaly Detection?	Feb 25, 2025	Anomaly DetectionIrregular Time Series	CodeCode Available	1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection	Aug 18, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Camouflaged Object Detection	Jun 1, 2020	Anomaly DetectionCamouflaged Object Segmentation	CodeCode Available	1
Locally Masked Convolution for Autoregressive Models	Jun 22, 2020	Anomaly DetectionDensity Estimation	CodeCode Available	1
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection	Jul 25, 2022	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Can LLMs Understand Time Series Anomalies?	Oct 7, 2024	Anomaly DetectionTime Series	CodeCode Available	1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences	Aug 14, 2022	Anomaly DetectionDecoder	CodeCode Available	1
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector	Nov 20, 2023	Anomaly DetectionBenchmarking	CodeCode Available	1
Challenges in Visual Anomaly Detection for Mobile Robots	Sep 22, 2022	Anomaly Detection	CodeCode Available	1
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection	Mar 23, 2023	3D Anomaly Detection and SegmentationAnomaly 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