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 611–620 of 4856 papers

Title	Date	Tasks	Status	Hype
Can LLMs Understand Time Series Anomalies?	Oct 7, 2024	Anomaly DetectionTime Series	CodeCode Available	1
Does Graph Distillation See Like Vision Dataset Counterpart?	Oct 13, 2023	Anomaly DetectionDataset Distillation	CodeCode Available	1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment	Feb 28, 2022	3D Action RecognitionAction Analysis	CodeCode Available	1
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection	Aug 17, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
DSR -- A dual subspace re-projection network for surface anomaly detection	Aug 2, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Anomaly Detection Requires Better Representations	Oct 19, 2022	3D Anomaly Detection and SegmentationAnomaly Detection	CodeCode Available	1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection	Aug 18, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection	Mar 28, 2022	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available	1
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays	Jun 8, 2022	Anomaly DetectionOne-Class Classification	CodeCode Available	1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection	Dec 19, 2024	Anomaly DetectionEEG	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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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