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 4471–4480 of 4856 papers

Title	Date	Tasks	Status
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training	May 17, 2018	Anomaly DetectionDecoder	CodeCode Available
GANetic Loss for Generative Adversarial Networks with a Focus on Medical Applications	Jun 7, 2024	Anomaly DetectionImage Generation	CodeCode Available
SECODA: Segmentation- and Combination-Based Detection of Anomalies	Aug 16, 2020	Anomaly DetectionAttribute	CodeCode Available
Anomaly Detection through Unsupervised Federated Learning	Sep 9, 2022	Anomaly DetectionFederated Learning	CodeCode Available
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization	Sep 28, 2017	Anomaly DetectionQuantization	CodeCode Available
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection	Aug 17, 2023	Anomaly DetectionMixture-of-Experts	CodeCode Available
Beyond Conventional Transformers: The Medical X-ray Attention (MXA) Block for Improved Multi-Label Diagnosis Using Knowledge Distillation	Apr 3, 2025	Anomaly DetectionKnowledge Distillation	CodeCode Available
ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection	Aug 29, 2023	Anomaly DetectionSupervised Anomaly Detection	CodeCode Available
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories	Mar 17, 2023	Anomaly DetectionGroup Anomaly Detection	CodeCode Available
Deep One-Class Classification	Jul 1, 2018	Anomaly DetectionClassification	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