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 2621–2630 of 4856 papers

Title	Date	Tasks	Status	Hype
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach	Aug 17, 2022	Anomaly DetectionAttribute	CodeCode Available	0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success	Aug 16, 2022	Anomaly DetectionData Augmentation	CodeCode Available	0
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences	Aug 14, 2022	Anomaly DetectionDecoder	CodeCode Available	1
Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection	Aug 11, 2022	Anomaly DetectionFace Morphing Attack Detection	CodeCode Available	1
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection	Aug 11, 2022	Anomaly DetectionMultiple Instance Learning	—Unverified	0
Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells	Aug 11, 2022	Anomaly DetectionAnomaly Segmentation	—Unverified	0
Neural Embedding: Learning the Embedding of the Manifold of Physics Data	Aug 10, 2022	Anomaly Detection	—Unverified	0
Machine Learning with DBOS	Aug 10, 2022	Anomaly Detectionimage-classification	—Unverified	0
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection	Aug 8, 2022	Anomaly DetectionDefect Detection	—Unverified	0
SIAD: Self-supervised Image Anomaly Detection System	Aug 8, 2022	Anomaly DetectionCloud Computing	—Unverified	0

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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