Anomaly Detection
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zechenghe/CloudShield
https://github.com/zehuichen123/DSEBM
https://github.com/zehuichen123/MEG_tf
https://github.com/zhengant/vae
https://github.com/zhihanyang2022/aevb-tutorial
https://github.com/zhn6818/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/zhouzheyuan/r3d-ad
R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection
https://github.com/zhuyulin1994/BinarizedAttack
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
https://github.com/zichuan-liu/contralsp
https://github.com/zillow/luminaire
Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment
https://github.com/ziyouzheilu/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ziyunliang/itermask2
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
https://github.com/ziyunliang/itermasks2
Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
https://github.com/zjunet/amnet
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
https://github.com/zjysteven/mixoe
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/zqhang/Accurate-WinCLIP-pytorch
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
https://github.com/zqhang/WinCLIP-pytorch
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
https://github.com/zxhhh97/ABot
https://gitlab.com/bpaassen/ast2vec
https://gitlab.com/inpefess/ast2vec
https://huggingface.co/Muyiiiii-HF/WWW25-Grad
https://huggingface.co/spaces/DFisch/Image-Manipulation-Detection
DF-Net: The Digital Forensics Network for Image Forgery Detection
https://zenodo.org/record/8142658
DF-Net: The Digital Forensics Network for Image Forgery Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.