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 42764300 of 4856 papers

TitleStatusHype
Mitigating Spurious Negative Pairs for Robust Industrial Anomaly DetectionCode0
MIXAD: Memory-Induced Explainable Time Series Anomaly DetectionCode0
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type DataCode0
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty DetectionCode0
Image-Based Detection of Modifications in Gas Pump PCBs with Deep Convolutional AutoencodersCode0
MixedTeacher : Knowledge Distillation for fast inference textural anomaly detectionCode0
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory AnalysisCode0
Building and Interpreting Deep Similarity ModelsCode0
Trustworthy Prediction with Gaussian Process Knowledge ScoresCode0
Variational Autoencoder based Anomaly Detection using Reconstruction ProbabilityCode0
Image anomaly detection with capsule networks and imbalanced datasetsCode0
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer StudyCode0
Detecting Outliers with Poisson Image InterpolationCode0
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance InspectionCode0
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification FrameworkCode0
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based ApproachCode0
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse RepresentationsCode0
ModeConv: A Novel Convolution for Distinguishing Anomalous and Normal Structural BehaviorCode0
Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly DetectionCode0
Revisiting Graph Contrastive Learning for Anomaly DetectionCode0
Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural PerspectiveCode0
Model Extraction Attacks on Graph Neural Networks: Taxonomy and RealizationCode0
Revisiting Multi-Permutation Equivariance through the Lens of Irreducible RepresentationsCode0
Revisiting Non-separable Binary Classification and its Applications in Anomaly DetectionCode0
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI SystemsCode0
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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