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 1881–1890 of 4856 papers

Title	Date	Tasks	Status
Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection	Dec 10, 2021	Anomaly DetectionEvolutionary Algorithms	—Unverified
Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework	Mar 28, 2024	Anomaly DetectionLanguage Modeling	—Unverified
Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies	Sep 16, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Enhancing anomaly detection with topology-aware autoencoders	Feb 14, 2025	Anomaly Detection	—Unverified
Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition	Jul 15, 2023	Anomaly DetectionKeyword Spotting	—Unverified
Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection	Dec 11, 2024	Anomaly Detection	—Unverified
Anomaly Detection with Tensor Networks	Jun 3, 2020	Anomaly DetectionComputational Efficiency	—Unverified
Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning	Apr 2, 2024	Anomaly Detection	—Unverified
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection	Feb 12, 2025	Anomaly DetectionAttribute	—Unverified
Curved Geometric Networks for Visual Anomaly Recognition	Aug 2, 2022	Anomaly DetectionAnomaly Segmentation	—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