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 1051–1060 of 4856 papers

Title	Date	Tasks	Status
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation	Oct 15, 2024	Anomaly DetectionRed Teaming	—Unverified
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation	Oct 10, 2020	Anomaly DetectionOutlier Detection	—Unverified
Activity-Guided Industrial Anomalous Sound Detection against Interferences	Sep 3, 2024	Anomaly Detection	—Unverified
A Unifying Review of Deep and Shallow Anomaly Detection	Sep 24, 2020	Anomaly DetectionOne-Class Classification	—Unverified
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning	Sep 25, 2019	Anomaly DetectionDensity Estimation	—Unverified
A flexible outlier detector based on a topology given by graph communities	Feb 18, 2020	Anomaly Detection	—Unverified
Abnormal Event Detection In Videos Using Deep Embedding	Sep 15, 2024	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
Collaborative Anomaly Detection	Sep 20, 2022	Anomaly DetectionDensity Estimation	—Unverified
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis	Dec 5, 2023	Anomaly DetectionCrowd Counting	—Unverified
Anomaly Detection Based on Selection and Weighting in Latent Space	Mar 8, 2021	Anomaly Detection	—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