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 2641–2650 of 4856 papers

Title	Date	Tasks	Status	Hype
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions	Aug 1, 2022	3D Object DetectionAnomaly Detection	—Unverified	0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade	Jul 31, 2022	Anomaly DetectionContrastive Learning	—Unverified	0
Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers	Jul 29, 2022	Anomaly Detectionregression	—Unverified	0
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation	Jul 28, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	2
Look at Adjacent Frames: Video Anomaly Detection without Offline Training	Jul 27, 2022	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Learning Appearance-motion Normality for Video Anomaly Detection	Jul 27, 2022	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets	Jul 27, 2022	Anomaly Detection	—Unverified	0
Task Agnostic and Post-hoc Unseen Distribution Detection	Jul 26, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified	0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety	Jul 26, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection	Jul 25, 2022	Anomaly DetectionOne-Class Classification	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