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 1401–1410 of 4856 papers

Title	Date	Tasks	Status	Score
Combining Machine Learning Models using combo Library	Sep 21, 2019	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	5
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization	Oct 16, 2024	Anomaly DetectionFederated Learning	CodeCode Available	5
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD	Nov 23, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	5
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks	May 21, 2021	Anomaly Detectionimage-classification	CodeCode Available	5
An anomaly detection approach for backdoored neural networks: face recognition as a case study	Aug 22, 2022	Anomaly DetectionFace Recognition	CodeCode Available	5
Feature space reduction as data preprocessing for the anomaly detection	Mar 13, 2022	Anomaly Detection	CodeCode Available	5
FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks	Dec 13, 2024	Anomaly Detection	CodeCode Available	5
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection	Jul 5, 2024	Anomaly DetectionEdge-computing	CodeCode Available	5
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients	Mar 21, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	5
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications	May 30, 2018	Anomaly DetectionComputational Efficiency	CodeCode Available	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