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 1051–1060 of 4856 papers

Title	Date	Tasks	Status	Score
A Survey on GANs for Anomaly Detection	Jun 27, 2019	Anomaly DetectionSurvey	CodeCode Available	5
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature	Jan 6, 2021	Anomaly DetectionAnomaly Localization	CodeCode Available	5
Improving Vision Anomaly Detection with the Guidance of Language Modality	Oct 4, 2023	Anomaly DetectionDefect Detection	CodeCode Available	5
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark	May 4, 2020	Anomaly Detection	CodeCode Available	5
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localization	Mar 30, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	5
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization	May 10, 2019	Anomaly DetectionRetrieval	CodeCode Available	5
Improving Novelty Detection using the Reconstructions of Nearest Neighbours	Nov 11, 2021	Anomaly DetectionData Augmentation	CodeCode Available	5
Improved Anomaly Detection by Using the Attention-Based Isolation Forest	Oct 5, 2022	Anomaly Detection	CodeCode Available	5
Improved Anomaly Detection through Conditional Latent Space VAE Ensembles	Oct 16, 2024	Anomaly Detection	CodeCode Available	5
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection	May 14, 2021	Anomaly Detectionvalid	CodeCode Available	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