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 261–270 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers	Apr 12, 2021	Anomaly Detection	CodeCode Available	1	5
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization	Apr 8, 2021	Anomaly ClassificationAnomaly Detection	CodeCode Available	1	5
Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection	Jan 9, 2024	Anomaly DetectionDenoising	CodeCode Available	1	5
AD-LLM: Benchmarking Large Language Models for Anomaly Detection	Dec 15, 2024	Anomaly DetectionBenchmarking	CodeCode Available	1	5
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	Oct 7, 2016	Anomaly DetectionAutomatic Speech Recognition	CodeCode Available	1	5
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning	Mar 17, 2024	Anomaly Detection	CodeCode Available	1	5
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series	Apr 17, 2024	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark	Jun 13, 2024	Anomaly DetectionARC	CodeCode Available	1	5
A Discrepancy Aware Framework for Robust Anomaly Detection	Oct 11, 2023	Anomaly DetectionDecoder	CodeCode Available	1	5
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Dec 2, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1	5

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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