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 521–530 of 4856 papers

Title	Date	Tasks	Status	Hype
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
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification	Jan 1, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1
Revisiting Reverse Distillation for Anomaly Detection	Jan 1, 2023	Anomaly Detection	CodeCode Available	1
Anomaly Detection using Score-based Perturbation Resilience	Jan 1, 2023	Anomaly DetectionDenoising	CodeCode Available	1
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection	Jan 1, 2023	Anomaly DetectionKnowledge Distillation	CodeCode Available	1
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks	Dec 23, 2022	Anomaly DetectionPrivacy Preserving	CodeCode Available	1
SiteFerret: beyond simple pocket identification in proteins	Dec 22, 2022	Anomaly DetectionClustering	CodeCode Available	1
CHAD: Charlotte Anomaly Dataset	Dec 19, 2022	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
FedTADBench: Federated Time-Series Anomaly Detection Benchmark	Dec 19, 2022	Anomaly DetectionFederated Learning	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