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 3441–3450 of 4856 papers

Title	Date	Tasks	Status	Hype
Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression	Apr 14, 2021	Anomaly Detectionregression	—Unverified	0
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks	Apr 14, 2021	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning	Apr 13, 2021	Anomaly DetectionGaussian Processes	CodeCode Available	0
Mixed supervision for surface-defect detection: from weakly to fully supervised learning	Apr 13, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1
Anomaly Detection in Image Datasets Using Convolutional Neural Networks, Center Loss, and Mahalanobis Distance	Apr 13, 2021	Anomaly Detectionimage-classification	—Unverified	0
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation	Apr 12, 2021	Anomaly DetectionCardiac Segmentation	CodeCode Available	0
Using a Neural Network to Detect Anomalies given an N-gram Profile	Apr 12, 2021	Anomaly Detection	—Unverified	0
LearningCity: Knowledge Generation for Smart Cities	Apr 12, 2021	Anomaly Detection	—Unverified	0
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers	Apr 12, 2021	Anomaly Detection	CodeCode Available	1
A Principled Approach to Enriching Security-related Data for Running Processes through Statistics and Natural Language Processing	Apr 11, 2021	Anomaly Detection	CodeCode Available	1

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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