SOTAVerified

Anomaly Classification

Anomaly Classification is the task of identifying and categorizing different types of anomalies in visual data, rather than simply detecting whether an input is normal or anomalous. Unlike anomaly detection, which is typically a binary classification (normal vs. anomaly), anomaly classification requires distinguishing between multiple anomaly classes—each representing a distinct type of anomaly or irregularity. This task is critical in real-world applications such as industrial inspection, where different anomalies may require different responses or interventions.

Papers

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Showing 1–50 of 72 papers

TitleStatusHype
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled ImagesCode3
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical ImagesCode3
WinCLIP: Zero-/Few-Shot Anomaly Classification and SegmentationCode2
MedIAnomaly: A comparative study of anomaly detection in medical imagesCode2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language ModelsCode2
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
SoftPatch+: Fully Unsupervised Anomaly Classification and SegmentationCode2
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
Towards Total Recall in Industrial Anomaly DetectionCode2
Few-Shot Anomaly-Driven Generation for Anomaly Classification and SegmentationCode2
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADCode2
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionCode1
Component-aware anomaly detection framework for adjustable and logical industrial visual inspectionCode1
Sub-Image Anomaly Detection with Deep Pyramid CorrespondencesCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and SegmentationCode1
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine LearningCode1
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly DetectionCode0
Multi-Class Abnormality Classification Task in Video Capsule EndoscopyCode0
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRICode0
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound DiagnosisCode0
BINet: Multi-perspective Business Process Anomaly ClassificationCode0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
A Prototype-Based Neural Network for Image Anomaly Detection and LocalizationCode0
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial NetworksCode0
Fence GAN: Towards Better Anomaly DetectionCode0
Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect CommunicationCode0
Video Anomaly Detection with Structured KeywordsCode0
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIPCode0
A Cytology Dataset for Early Detection of Oral Squamous Cell CarcinomaCode0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence—0
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification—0
WEAC: Word embeddings for anomaly classification from event logs—0
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks—0
Anomaly Classification in Distribution Networks Using a Quotient Gradient System—0
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images—0
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection—0
CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection—0
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
Circuit design in biology and machine learning. II. Anomaly detection—0
Classification of Anomalies in Telecommunication Network KPI Time Series—0
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation—0
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers—0
Conditioning Latent-Space Clusters for Real-World Anomaly Classification—0
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All datasetsGoodsADMVTecADMVTec-ACVisA-AC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PatchCore-100%AUPR86.1—Unverified
2MiniMaxAD-frAUROC86.1—Unverified
3PatchCore-1%AUPR83.3—Unverified
4SimpleNetAUPR78.7—Unverified
5CFLOW-ADAUPR75.3—Unverified
6NSAAUPR71.8—Unverified
7DRAEMAUPR71—Unverified
8SPADEAUPR68.7—Unverified
9RD4ADAUPR68.2—Unverified
10f-AnoGANAUPR66.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VELMAccuracy (% )81.4—Unverified
2EchoAccuracy (% )72.9—Unverified
#ModelMetricClaimedVerifiedStatus
1VELMAccuracy (% )84—Unverified
#ModelMetricClaimedVerifiedStatus
1VELMAccuracy(%)69.6—Unverified