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 4511–4520 of 4856 papers

Title	Date	Tasks	Status
OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection	Dec 4, 2023	Anomaly Detectionbackdoor defense	CodeCode Available
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms	Feb 25, 2025	Anomaly Detection	CodeCode Available
DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent Features	Mar 5, 2025	Anomaly DetectionData Augmentation	CodeCode Available
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects	Apr 26, 2021	Anomaly DetectionImage Reconstruction	CodeCode Available
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations	Mar 15, 2024	Anomaly DetectionDecoder	CodeCode Available
An Adaptive Anaphylaxis Detection and Emergency Response System	Aug 5, 2019	Anomaly Detection	CodeCode Available
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost	Jul 16, 2019	Anomaly DetectionFederated Learning	CodeCode Available
OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences	Feb 7, 2024	Anomaly DetectionBehavioural cloning	CodeCode Available
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interest	Mar 3, 2025	Anomaly DetectionTime Series	CodeCode Available
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation	Jan 30, 2023	Anomaly DetectionKnowledge Distillation	CodeCode Available

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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