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

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Showing 31–40 of 4856 papers

Title	Date	Tasks	Status	Hype
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond	Mar 21, 2024	Anomaly DetectionDeep Learning	CodeCode Available	3
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection	Mar 19, 2024	Anomaly DetectionBenchmarking	CodeCode Available	3
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images	Mar 19, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	3
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts	Mar 11, 2024	Anomaly Detection	CodeCode Available	3
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection	Mar 9, 2024	Anomaly Detectionfeature selection	CodeCode Available	3
TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis	Feb 26, 2024	Anomaly DetectionImputation	CodeCode Available	3
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images	Jan 30, 2024	Anomaly ClassificationAnomaly Detection	CodeCode Available	3
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models	Aug 29, 2023	Anomaly DetectionIn-Context Learning	CodeCode Available	3
AER: Auto-Encoder with Regression for Time Series Anomaly Detection	Dec 27, 2022	Anomaly DetectionBenchmarking	CodeCode Available	3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model	Nov 1, 2022	Anomaly DetectionBrain Tumor Segmentation	CodeCode Available	3

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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