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

Showing 701750 of 4856 papers

TitleStatusHype
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionCode1
Graph Contrastive Learning for Anomaly DetectionCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly ScreeningCode1
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature MappingCode1
Log-based Anomaly Detection Without Log ParsingCode1
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene SegmentationCode1
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-SupervisionCode1
Neural Contextual Anomaly Detection for Time SeriesCode1
Experience Report: Deep Learning-based System Log Analysis for Anomaly DetectionCode1
Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep LearningCode1
TS2Vec: Towards Universal Representation of Time SeriesCode1
Deep Learning in Latent Space for Video Prediction and CompressionCode1
Anomaly Detection in Dynamic Graphs via TransformerCode1
Anomaly Detection in Video Sequences: A Benchmark and Computational ModelCode1
FastAno: Fast Anomaly Detection via Spatio-temporal Patch TransformationCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian FilteringCode1
A Comprehensive Survey on Graph Anomaly Detection with Deep LearningCode1
MLPerf Tiny BenchmarkCode1
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
Implicit field learning for unsupervised anomaly detection in medical imagesCode1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
XBNet : An Extremely Boosted Neural NetworkCode1
Sketch-Based Anomaly Detection in Streaming GraphsCode1
MemStream: Memory-Based Streaming Anomaly DetectionCode1
Mean-Shifted Contrastive Loss for Anomaly DetectionCode1
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditionsCode1
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly SegmentationCode1
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Masked Contrastive Learning for Anomaly DetectionCode1
Vision Transformers are Robust LearnersCode1
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Unsupervised Offline Changepoint Detection EnsemblesCode1
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly DetectionCode1
Inpainting Transformer for Anomaly DetectionCode1
The 5th AI City ChallengeCode1
Anomaly Detection for Solder Joints Using β-VAECode1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data StreamsCode1
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and LocalizationCode1
What is Wrong with One-Class Anomaly Detection?Code1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
ADNet: Temporal Anomaly Detection in Surveillance VideosCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified