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 951–960 of 4856 papers

Title	Date	Tasks	Status	Score
Learning normal asymmetry representations for homologous brain structures	Jun 27, 2023	Anomaly DetectionRepresentation Learning	CodeCode Available	5
Anomaly Detection by Recombining Gated Unsupervised Experts	Aug 31, 2020	Anomaly DetectionMixture-of-Experts	CodeCode Available	5
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection	Jun 1, 2018	Anomaly DetectionImage Generation	CodeCode Available	5
Learning Neural Representations for Network Anomaly Detection	Aug 1, 2019	Anomaly DetectionIntrusion Detection	CodeCode Available	5
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights	Mar 14, 2023	Anomaly DetectionContinual Learning	CodeCode Available	5
Long Short Term Memory Networks for Anomaly Detection in Time Series	Apr 22, 2015	Anomaly DetectionFault Detection	CodeCode Available	5
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection	Jun 15, 2018	Anomaly Detection	CodeCode Available	5
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis	Mar 20, 2022	Anomaly DetectionMedical Image Analysis	CodeCode Available	5
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification	Jan 8, 2019	Anomaly DetectionClassification	CodeCode Available	5
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping	Dec 5, 2023	Anomaly Detection	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