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 851–860 of 4856 papers

Title	Date	Tasks	Status	Hype
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling	Feb 6, 2019	Anomaly DetectionAttribute	CodeCode Available	1
BMAD: Benchmarks for Medical Anomaly Detection	Jun 20, 2023	Anomaly DetectionMedical Diagnosis	CodeCode Available	1
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations	May 30, 2024	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors	Jun 21, 2023	Anomaly DetectionDecoder	CodeCode Available	1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	Apr 19, 2021	Anomaly DetectionOutlier Detection	CodeCode Available	1
Beyond Individual Input for Deep Anomaly Detection on Tabular Data	May 24, 2023	Anomaly Detection	CodeCode Available	1
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation	Jun 13, 2025	Anomaly DetectionClustering	CodeCode Available	1
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection	Mar 18, 2024	Anomaly DetectionDenoising	CodeCode Available	1
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection	Sep 25, 2022	Anomaly DetectionEvent Detection	CodeCode Available	1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning	Dec 17, 2024	Anomaly Detection	CodeCode Available	1

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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