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 4251–4300 of 4856 papers

TitleStatusHype
Triggering Dark Showers with Conditional Dual Auto-EncodersCode0
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive LearningCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the EdgeCode0
Synthetic Data Generation for Anomaly Detection on Table GrapesCode0
Meta-survey on outlier and anomaly detectionCode0
Improved Anomaly Detection by Using the Attention-Based Isolation ForestCode0
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly DetectionCode0
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal SamplesCode0
Metric Learning for Novelty and Anomaly DetectionCode0
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic ImagesCode0
Detecting semantic anomaliesCode0
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical PerspectiveCode0
Importance Weighted Adversarial Discriminative Transfer for Anomaly DetectionCode0
Bump Hunting in Latent SpaceCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment PerspectiveCode0
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson LemmaCode0
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple RemedyCode0
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Imbalanced Graph-Level Anomaly Detection via Counterfactual Augmentation and Feature LearningCode0
syslrn: Learning What to Monitor for Efficient Anomaly DetectionCode0
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL DatasetCode0
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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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