SOTAVerified

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

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Showing 1501–1550 of 4856 papers

TitleStatusHype
Image-based Deep Learning for Smart Digital Twins: a Review—0
Distillation-based fabric anomaly detectionCode0
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems—0
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low ToleranceCode1
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks—0
Regression Based Anomaly Detection in Electric Vehicle State of Charge Fluctuations Through Analysis of EVCI Data—0
Unsupervised Continual Anomaly Detection with Contrastively-learned PromptCode2
Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware DetectorCode0
Pre-training Vision Models with Mandelbulb VariationsCode0
Hyperbolic Anomaly Detection—0
Uncovering What Why and How: A Comprehensive Benchmark for Causation Understanding of Video AnomalyCode2
Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly DetectionCode0
Error Detection in Egocentric Procedural Task Videos—0
Towards Surveillance Video-and-Language Understanding: New Dataset Baselines and Challenges—0
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning—0
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models—0
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications—0
Sensor Data Simulation for Anomaly Detection of the Elderly Living AloneCode0
Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammogramsCode0
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection—0
Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics—0
Soft Contrastive Learning for Time SeriesCode1
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
Abnormal component analysis—0
TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation LearningCode1
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
Understanding normalization in contrastive representation learning and out-of-distribution detectionCode0
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly DetectionCode1
Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis—0
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection—0
Invariant Anomaly Detection under Distribution Shifts: A Causal PerspectiveCode1
Fast kernel half-space depth for data with non-convex supports—0
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly DetectionCode1
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Self-supervised Complex Network for Machine Sound Anomaly Detection—0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
Machine Learning for Anomaly Detection in Particle Physics—0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Produce Once, Utilize Twice for Anomaly Detection—0
When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly DetectionCode1
A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities—0
Residual ANODE—0
PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly ExplanationCode0
Label-Free Multivariate Time Series Anomaly DetectionCode1
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks—0
TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration NetworkCode1
Entropy Causal Graphs for Multivariate Time Series Anomaly DetectionCode1
Deep Anomaly Detection in Text—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified