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 2171–2180 of 4856 papers

Title	Date	Tasks	Status
Context-aware Domain Adaptation for Time Series Anomaly Detection	Apr 15, 2023	Anomaly DetectionDeep Reinforcement Learning	—Unverified
Anomaly Detection using Ensemble Classification and Evidence Theory	Dec 23, 2022	Anomaly DetectionClassification	—Unverified
Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies	Dec 24, 2020	Anomaly DetectionClustering	—Unverified
Anomaly Detection using Edge Computing in Video Surveillance System: Review	Jul 6, 2021	Anomaly DetectionEdge-computing	—Unverified
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection	Oct 15, 2024	Anomaly DetectionContrastive Learning	—Unverified
Anomaly detection using Diffusion-based methods	Dec 10, 2024	Anomaly DetectionDecoder	—Unverified
Constructing a meta-learner for unsupervised anomaly detection	Apr 22, 2023	Anomaly DetectionAutoML	—Unverified
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data	Mar 24, 2024	Anomaly DetectionVideo Anomaly Detection	—Unverified
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression	Oct 18, 2020	Anomaly DetectionLesion Detection	—Unverified
Generic Multi-modal Representation Learning for Network Traffic Analysis	May 4, 2024	Anomaly DetectionFeature Engineering	—Unverified

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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