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Multi-class Anomaly Detection
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Multi-class Anomaly Detection is a task that identifies anomalies by jointly learning and detecting outliers across multiple classes, in contrast to traditional Anomaly Detection, which typically focuses on identifying anomalies within a single class.
논문
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Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization
Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
mixed attention auto encoder for multi-class industrial anomaly detection
UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection
LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection
A Unified Model for Multi-class Anomaly Detection
Explainable multi-class anomaly detection on functional data
Multi-Class Anomaly Detection
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