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 101–110 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation	Feb 29, 2024	Anomaly DetectionDecoder	CodeCode Available	2	5
Log-based Anomaly Detection with Deep Learning: How Far Are We?	Feb 9, 2022	Anomaly DetectionDeep Learning	CodeCode Available	2	5
CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection	Aug 28, 2024	Anomaly DetectionSegmentation	CodeCode Available	2	5
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection	May 12, 2025	Anomaly Detection	CodeCode Available	2	5
MedIAnomaly: A comparative study of anomaly detection in medical images	Apr 6, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	2	5
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder	Nov 2, 2017	Anomaly Detection	CodeCode Available	2	5
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis	Aug 14, 2024	Anomaly DetectionBoundary Detection	CodeCode Available	2	5
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities	May 2, 2022	Anomaly DetectionDefect Detection	CodeCode Available	2	5
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching	Oct 16, 2024	Anomaly DetectionTime Series	CodeCode Available	2	5
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection	Jun 17, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	2	5

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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