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 881–890 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead	Nov 5, 2023	3D Anomaly DetectionAnomaly Detection	CodeCode Available	1	5
Deep Anomaly Detection Using Geometric Transformations	May 28, 2018	Anomaly Detection	CodeCode Available	1	5
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation	Nov 2, 2023	Anomaly DetectionDepth Anomaly Detection and Segmentation	CodeCode Available	1	5
Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection	Mar 28, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	1	5
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection	Oct 14, 2022	3D Anomaly DetectionAnomaly Detection	CodeCode Available	1	5
ERX: A Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line Scanning	Aug 27, 2024	Anomaly DetectionCPU	CodeCode Available	1	5
Active Anomaly Detection via Ensembles	Sep 17, 2018	Active LearningAnomaly Detection	CodeCode Available	1	5
Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention	Feb 14, 2023	Anomaly Detection	CodeCode Available	1	5
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection	Mar 20, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	1	5
Anomaly Detection in Aerial Videos with Transformers	Sep 25, 2022	Anomaly Detection	CodeCode Available	1	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