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 1151–1160 of 4856 papers

Title	Date	Tasks	Status
A Systematic Mapping Study in AIOps	Dec 15, 2020	Anomaly Detection	—Unverified
A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flows	Mar 11, 2025	Anomaly DetectionArticles	—Unverified
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network	Mar 5, 2021	Anomaly DetectionDefect Detection	—Unverified
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting	May 1, 2022	Anomaly Detection	—Unverified
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data	Feb 19, 2025	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Anomaly Detection and Automated Labeling for Voter Registration File Changes	Jun 16, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection	Oct 19, 2022	Anomaly DetectionAnomaly Localization	—Unverified
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect	Jan 29, 2024	Anomaly DetectionDiversity	—Unverified
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems	Apr 7, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images	Apr 24, 2022	Anomaly DetectionDefect Detection	—Unverified

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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