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Multi-class Anomaly Detection

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.

Papers

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Showing 26–39 of 39 papers

TitleStatusHype
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark—0
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization—0
mixed attention auto encoder for multi-class industrial anomaly detection—0
Multi-Class Anomaly Detection based on Regularized Discriminative Coupled hypersphere-based Feature Adaptation—0
Anomaly Detection for Scenario-based Insider Activities using CGAN Augmented Data—0
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment—0
OmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization—0
Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical TransientsCode0
Pyramid-based Mamba Multi-class Unsupervised Anomaly DetectionCode0
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly DetectionCode0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
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