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 2531–2540 of 4856 papers

Title	Date	Tasks	Status	Hype
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets	Oct 4, 2022	Anomaly DetectionMultiple Instance Learning	—Unverified	0
Unsupervised Model Selection for Time-series Anomaly Detection	Oct 3, 2022	Anomaly Detectionmodel	CodeCode Available	1
A Lightweight Yet Robust Approach to Textual Anomaly Detection	Oct 1, 2022	Anomaly Detection	—Unverified	0
Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback	Oct 1, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	0
Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional Autoencoders	Sep 30, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	0
Cardiotocography Signal Abnormality Detection based on Deep Unsupervised Models	Sep 29, 2022	Anomaly Detection	—Unverified	0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges	Sep 29, 2022	Anomaly DetectionGraph Anomaly Detection	—Unverified	0
Anomaly detection optimization using big data and deep learning to reduce false-positive	Sep 28, 2022	Anomaly DetectionIntrusion Detection	—Unverified	0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization	Sep 28, 2022	Anomaly DetectionChatbot	—Unverified	0
Spatio-Temporal Relation Learning for Video Anomaly Detection	Sep 27, 2022	Anomaly DetectionGraph Embedding	—Unverified	0

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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