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 631–640 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection	Oct 26, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1	5
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI	Sep 13, 2021	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1	5
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation	Nov 2, 2023	Anomaly DetectionDepth Anomaly Detection and Segmentation	CodeCode Available	1	5
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The Unknown	Jul 10, 2023	Anomaly DetectionCode Generation	CodeCode Available	1	5
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and Localization	Dec 16, 2024	Anomaly Detection	CodeCode Available	1	5
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection	Sep 24, 2020	Anomaly DetectionGroup Anomaly Detection	CodeCode Available	1	5
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance	Jan 3, 2024	Anomaly Detection	CodeCode Available	1	5
Classification-Based Anomaly Detection for General Data	May 5, 2020	Anomaly DetectionClassification	CodeCode Available	1	5
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	5
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection	Aug 6, 2023	Anomaly DetectionSelf-Supervised Learning	CodeCode Available	1	5

Show:10 25 50

← PrevPage 64 of 486Next →

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