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 3591–3600 of 4856 papers

Title	Date	Tasks	Status
Good Practices and A Strong Baseline for Traffic Anomaly Detection	May 9, 2021	Anomaly DetectionManagement	CodeCode Available
Energy-Based Anomaly Detection and Localization	May 7, 2021	Anomaly Detection	—Unverified
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning	May 5, 2021	Adversarial AttackAnomaly Detection	CodeCode Available
An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs	May 4, 2021	Action RecognitionAnomaly Detection	—Unverified
Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information	May 2, 2021	Anomaly DetectionDensity Estimation	—Unverified
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings	Apr 30, 2021	Anomaly DetectionOptical Flow Estimation	—Unverified
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities	Apr 30, 2021	Anomaly DetectionMulti-class Classification	—Unverified
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection	Apr 30, 2021	Anomaly DetectionClustering	—Unverified
Discriminative-Generative Dual Memory Video Anomaly Detection	Apr 29, 2021	Anomaly DetectionTriplet	—Unverified
PANDA : Perceptually Aware Neural Detection of Anomalies	Apr 28, 2021	Anomaly DetectionDefect 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	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