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 2321–2330 of 4856 papers

Title	Date	Tasks	Status	Hype
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection	Jan 7, 2023	Active LearningAnomaly Detection	CodeCode Available	0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization	Jan 5, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions	Jan 5, 2023	Anomaly Detection	—Unverified	0
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection	Jan 4, 2023	Anomaly DetectionDeepFake Detection	CodeCode Available	1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG	Jan 3, 2023	Anomaly DetectionEEG	CodeCode Available	1
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model	Jan 1, 2023	Anomaly Detectionmodel	—Unverified	0
Removing Anomalies as Noises for Industrial Defect Localization	Jan 1, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection	Jan 1, 2023	Anomaly DetectionKnowledge Distillation	CodeCode Available	1
Template-guided Hierarchical Feature Restoration for Anomaly Detection	Jan 1, 2023	Anomaly Detection	—Unverified	0
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification	Jan 1, 2023	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