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 841–850 of 4856 papers

Title	Date	Tasks	Status	Hype
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure	Jul 12, 2020	Anomaly DetectionAttribute	CodeCode Available	1
Anomaly Detection-Based Unknown Face Presentation Attack Detection	Jul 11, 2020	Anomaly DetectionFace Presentation Attack Detection	CodeCode Available	1
Few-Shot One-Class Classification via Meta-Learning	Jul 8, 2020	Anomaly DetectionClassification	CodeCode Available	1
Explainable Deep One-Class Classification	Jul 3, 2020	Anomaly DetectionClassification	CodeCode Available	1
Laplacian Change Point Detection for Dynamic Graphs	Jul 2, 2020	Anomaly DetectionChange Point Detection	CodeCode Available	1
Subject-Aware Contrastive Learning for Biosignals	Jun 30, 2020	Anomaly DetectionContrastive Learning	CodeCode Available	1
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion Detection	Jun 29, 2020	Anomaly DetectionIntrusion Detection	CodeCode Available	1
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation	Jun 29, 2020	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy	Jun 26, 2020	Anomaly Detection	CodeCode Available	1
Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection	Jun 26, 2020	Anomaly DetectionIntrusion Detection	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