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 381–390 of 4856 papers

Title	Date	Tasks	Status	Hype
Can Multimodal LLMs Perform Time Series Anomaly Detection?	Feb 25, 2025	Anomaly DetectionIrregular Time Series	CodeCode Available	1
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms	Feb 25, 2025	Anomaly Detection	CodeCode Available	0
A digital eye-fixation biomarker using a deep anomaly scheme to classify Parkisonian patterns	Feb 25, 2025	Anomaly DetectionSpecificity	—Unverified	0
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection	Feb 24, 2025	AnatomyAnomaly Detection	CodeCode Available	1
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning	Feb 23, 2025	Anomaly DetectionFederated Learning	—Unverified	0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving	Feb 23, 2025	Anomaly DetectionAutonomous Driving	—Unverified	0
Machine Learning-Based Cloud Computing Compliance Process Automation	Feb 22, 2025	Anomaly DetectionCloud Computing	—Unverified	0
DiffFake: Exposing Deepfakes using Differential Anomaly Detection	Feb 22, 2025	Anomaly DetectionBinary Classification	—Unverified	0
rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics	Feb 22, 2025	Anomaly Detection	—Unverified	0
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection	Feb 21, 2025	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	2

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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