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 371–380 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition	Jul 6, 2022	Anomaly DetectionOpen Set Learning	CodeCode Available	1	5
Backpropagated Gradient Representations for Anomaly Detection	Jul 18, 2020	Anomaly DetectionComputational Efficiency	CodeCode Available	1	5
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection	Mar 10, 2022	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	1	5
Explainable Deep Few-shot Anomaly Detection with Deviation Networks	Aug 1, 2021	Anomaly DetectionMultiple Instance Learning	CodeCode Available	1	5
ADNet: Temporal Anomaly Detection in Surveillance Videos	Apr 14, 2021	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1	5
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection	Apr 16, 2025	Anomaly DetectionLarge Language Model	CodeCode Available	1	5
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images	Mar 13, 2024	AnatomyAnomaly Detection	CodeCode Available	1	5
An End-to-End Computer Vision Methodology for Quantitative Metallography	Apr 22, 2021	Anomaly DetectionImage Inpainting	CodeCode Available	1	5
DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision	Jul 28, 2020	Anomaly Detection	CodeCode Available	1	5
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models	May 13, 2024	Anomaly DetectionEdge Detection	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	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