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 4251–4260 of 4856 papers

Title	Date	Tasks	Status
Triggering Dark Showers with Conditional Dual Auto-Encoders	Jun 22, 2023	Anomaly Detection	CodeCode Available
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning	Oct 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network	Jul 8, 2020	Anomaly DetectionAstronomy	CodeCode Available
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge	Sep 23, 2024	Anomaly DetectionVariational Inference	CodeCode Available
Synthetic Data Generation for Anomaly Detection on Table Grapes	Dec 17, 2024	Anomaly DetectionClassification	CodeCode Available
Meta-survey on outlier and anomaly detection	Dec 12, 2023	Anomaly DetectionBenchmarking	CodeCode Available
Improved Anomaly Detection by Using the Attention-Based Isolation Forest	Oct 5, 2022	Anomaly Detection	CodeCode Available
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection	May 13, 2024	Anomaly DetectionTime Series	CodeCode Available
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal Samples	Aug 24, 2024	Anomaly DetectionDecoder	CodeCode Available
Metric Learning for Novelty and Anomaly Detection	Aug 16, 2018	Anomaly DetectionMetric Learning	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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