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 841–850 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification	May 29, 2025	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection	Dec 4, 2024	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1	5
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers	Oct 7, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1	5
Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection	Oct 6, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1	5
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder	Jan 31, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1	5
F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information	Dec 3, 2024	Anomaly DetectionTime Series	CodeCode Available	1	5
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach	Aug 2, 2023	Anomaly DetectionDeep Learning	CodeCode Available	1	5
Future Frame Prediction for Anomaly Detection â A New Baseline	Jun 1, 2018	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1	5
Future Frame Prediction for Anomaly Detection -- A New Baseline	Dec 28, 2017	Anomaly DetectionOptical Flow Estimation	CodeCode Available	1	5
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection	Oct 14, 2022	3D Anomaly DetectionAnomaly 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