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 2251–2260 of 4856 papers

Title	Date	Tasks	Status
Harnessing Large Language Models for Training-free Video Anomaly Detection	Apr 1, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition	Feb 4, 2025	Anomaly DetectionGesture Recognition	—Unverified
ComplexVAD: Detecting Interaction Anomalies in Video	Jan 16, 2025	Anomaly DetectionVideo Anomaly Detection	—Unverified
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning	Oct 19, 2020	Anomaly Detection	—Unverified
Competing Topic Naming Conventions in Quora: Predicting Appropriate Topic Merges and Winning Topics from Millions of Topic Pairs	Sep 10, 2019	Anomaly DetectionTAG	—Unverified
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays	Jul 24, 2023	Anomaly DetectionOne-Class Classification	—Unverified
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection	Apr 9, 2025	Anomaly DetectionContrastive Learning	—Unverified
Comparison of RNN Encoder-Decoder Models for Anomaly Detection	Jul 17, 2018	Anomaly DetectionDecoder	—Unverified
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives	Jul 9, 2024	Anomaly DetectionDecoder	—Unverified
Anomaly Detection on IT Operation Series via Online Matrix Profile	Aug 27, 2021	Anomaly DetectionTime Series	—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