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 4591–4600 of 4856 papers

Title	Date	Tasks	Status
FADE: Forecasting for Anomaly Detection on ECG	Feb 11, 2025	Anomaly DetectionDomain Adaptation	CodeCode Available
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images	Aug 10, 2018	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available
Semantic Novelty Detection in Natural Language Descriptions	Nov 1, 2021	Anomaly DetectionNovelty Detection	CodeCode Available
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients	Mar 21, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available
Semi-automatic staging area for high-quality structured data extraction from scientific literature	Sep 19, 2023	Anomaly Detection	CodeCode Available
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings	Nov 6, 2019	Anomaly DetectionAnomaly Segmentation	CodeCode Available
DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models	Jun 22, 2024	Anomaly Detection	CodeCode Available
Backdoor Attack against One-Class Sequential Anomaly Detection Models	Feb 15, 2024	Anomaly DetectionBackdoor Attack	CodeCode Available
(1 + )-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets	Jun 14, 2019	Anomaly DetectionGeneral Classification	CodeCode Available
On the True Distribution Approximation of Minimum Bayes-Risk Decoding	Mar 31, 2024	Anomaly DetectionText Generation	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