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 2051–2075 of 4856 papers

Title	Date	Tasks	Status	Hype
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD	May 27, 2023	Anomaly ClassificationAnomaly Detection	CodeCode Available	2
Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation	May 27, 2023	Airbubbles DetectionAnomaly Detection	CodeCode Available	0
Single-Model Attribution of Generative Models Through Final-Layer Inversion	May 26, 2023	Anomaly Detection	CodeCode Available	0
Improving Position Encoding of Transformers for Multivariate Time Series Classification	May 26, 2023	Anomaly DetectionPosition	CodeCode Available	1
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection	May 26, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics	May 25, 2023	Anomaly DetectionFraud Detection	CodeCode Available	1
Anomaly Detection in Satellite Videos using Diffusion Models	May 25, 2023	Anomaly DetectionManagement	—Unverified	0
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series	May 25, 2023	Anomaly DetectionTime Series	—Unverified	0
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning	May 25, 2023	Anomaly Detection	CodeCode Available	1
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision	May 25, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection	May 25, 2023	Anomaly DetectionModel Optimization	—Unverified	0
Anomaly Detection with Conditioned Denoising Diffusion Models	May 25, 2023	Anomaly DetectionDenoising	CodeCode Available	2
Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos	May 24, 2023	Anomaly Detection	CodeCode Available	0
Multiresolution Feature Guidance Based Transformer for Anomaly Detection	May 24, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Real time dense anomaly detection by learning on synthetic negative data	May 24, 2023	Anomaly Detection	—Unverified	0
Beyond Individual Input for Deep Anomaly Detection on Tabular Data	May 24, 2023	Anomaly Detection	CodeCode Available	1
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation	May 23, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs	May 23, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection	May 22, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices	May 21, 2023	Anomaly Detection	—Unverified	0
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection	May 21, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing	May 20, 2023	Anomaly DetectionCPU	—Unverified	0
Segment Any Anomaly without Training via Hybrid Prompt Regularization	May 18, 2023	Anomaly DetectionAnomaly Localization	CodeCode Available	2
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection	May 18, 2023	Anomaly DetectionFew-Shot Learning	CodeCode Available	0
Reconstruction Error-based Anomaly Detection with Few Outlying Examples	May 17, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0

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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