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 151–160 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Detection with Conditioned Denoising Diffusion Models	May 25, 2023	Anomaly DetectionDenoising	CodeCode Available	2
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection	Mar 25, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
Large language models can be zero-shot anomaly detectors for time series?	May 23, 2024	Anomaly DetectionLanguage Modeling	CodeCode Available	2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering	May 2, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection	Jun 17, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	2
Log-based Anomaly Detection with Deep Learning: How Far Are We?	Feb 9, 2022	Anomaly DetectionDeep Learning	CodeCode Available	2
LogLLM: Log-based Anomaly Detection Using Large Language Models	Nov 13, 2024	Anomaly DetectionDecoder	CodeCode Available	2
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection	Dec 11, 2023	Anomaly DetectionDenoising	CodeCode Available	2
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models	Jul 14, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	2
ZClip: Adaptive Spike Mitigation for LLM Pre-Training	Apr 3, 2025	Anomaly DetectionLarge Language Model	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