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Multi-class Anomaly Detection

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보존된 평가 기록: 14

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/cool-xuan/vqflow

공식 코드

VQ-Flow: Taming Normalizing Flows for Multi-Class Anomaly Detection via Hierarchical Vector Quantization

https://github.com/cqylunlun/CRAS

공식 코드

Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection

https://github.com/easyoo/omni-ad

공식 코드

Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection

https://github.com/guojiajeremy/dinomaly

공식 코드

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

https://github.com/hlr7999/rlr

공식 코드

Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection

https://github.com/lewandofskee/DiAD

공식 코드

DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

https://github.com/lewandofskee/mambaad

공식 코드

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

https://github.com/luow23/inp-former

공식 코드

Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection

https://github.com/nothingtolose9979/ras

공식 코드

Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection

https://github.com/rithwik-g/astromcad

공식 코드

A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients

https://github.com/ruiyinglu/hvq-trans

공식 코드

Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

https://github.com/xcyao00/hgad

공식 코드

Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection

https://github.com/yoojlee/uniformaly

공식 코드

UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection

https://github.com/zhangzjn/ader

공식 코드

Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection

https://github.com/zhiyuanyou/uniad

공식 코드

A Unified Model for Multi-class Anomaly Detection

https://github.com/Fantasyele/LLaVA-KD

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

https://github.com/septmars/DL

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

https://github.com/sushovanjena/Attention-aware-Entropy-Distillation-for-Anomaly-Detection

Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.