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 3461–3470 of 4856 papers

Title	Date	Tasks	Status
Unsupervised Anomaly Detection via Nonlinear Manifold Learning	Jun 15, 2023	Anomaly DetectionNovelty Detection	—Unverified
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers	Mar 29, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape	Dec 21, 2020	Anomaly DetectionBinary Classification	—Unverified
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation	Mar 16, 2021	AnatomyAnomaly Detection	—Unverified
Unsupervised Brain Anomaly Detection and Segmentation with Transformers	Feb 23, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks	Feb 18, 2021	Anomaly DetectionClustering	—Unverified
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models	Apr 1, 2019	Anomaly DetectionContextual Anomaly Detection	—Unverified
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series	Apr 17, 2020	Anomaly DetectionOutlier Detection	—Unverified
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals	Jul 27, 2021	Activity RecognitionAnomaly Detection	—Unverified
Unsupervised Deep Learning for IoT Time Series	Feb 7, 2023	Anomaly DetectionClustering	—Unverified

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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