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 4051–4060 of 4856 papers

Title	Date	Tasks	Status
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in Images	Jul 20, 2021	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available
Combining Machine Learning Models using combo Library	Sep 21, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection	Jul 13, 2023	Anomaly DetectionData Augmentation	CodeCode Available
Leveraging Log Instructions in Log-based Anomaly Detection	Jul 7, 2022	Anomaly DetectionCPU	CodeCode Available
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture	Apr 18, 2020	Anomaly DetectionClassification	CodeCode Available
Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease	Aug 4, 2024	AnatomyAnomaly Detection	CodeCode Available
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection	Jul 17, 2024	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Are We Using Autoencoders in a Wrong Way?	Sep 4, 2023	Anomaly DetectionClassification	CodeCode Available
Less-supervised learning with knowledge distillation for sperm morphology analysis	May 8, 2024	Anomaly DetectionKnowledge Distillation	CodeCode Available
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework	Oct 1, 2017	Anomaly DetectionDiversity	CodeCode Available

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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