https://github.com/CuberrChen/STFPM-Paddle
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Papers with Code 아카이브
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/D3-AI/Orion
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
https://github.com/DASH-Lab/VFP290K
https://github.com/DIDSR/DomId
https://github.com/Dai7Igarashi/Anomaly-Detection
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/Daniel-H-99/CRD
https://github.com/Daniel00008/WS-VAD-mindspore
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
https://github.com/DannisZgggg/AnoGAN-WGAN-pytorch
https://github.com/Darcyddx/VAD-LLM
Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight
https://github.com/DegardinBruno/human_self_learning_anomaly
https://github.com/DesignInformaticsLab/Morphology-Aware-Network
https://github.com/Discover304/SinGlow
https://github.com/DylanSpicker/STAT923-Final-Project
https://github.com/DylanSpicker/judging-covers
https://github.com/ELongking/Memseg-Implementation
https://github.com/ENSTA-U2IS/MUAD-Dataset
https://github.com/Eacient/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Ehteshamciitwah/MSFR
https://github.com/EighteenSprings/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ElliottKasoar/GeneVAE
https://github.com/ElliottKasoar/gene-dag-vae
https://github.com/Emyyr/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ErikKratzCth/ALOCC_Keras_SMILE
Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/vae/vae.py
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Android
https://github.com/Fantasyele/LLaVA-KD
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
https://github.com/Faruk-Ahmed/detecting_semantic_anomalies
https://github.com/FishSmile-syx/ITAE-Pytorch-Anomaly_Detection
https://github.com/FlappyPeggy/Registration-based-PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/FlorentinCDX/pytorch-mnist-VAE
https://github.com/ForrestPi/anomalyLocalization
Iterative energy-based projection on a normal data manifold for anomaly localization
https://github.com/FourthM/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/Francois-Aubet/gluon-ts/blob/monte_carlo_em_masking_notebook/src/gluonts/nursery/anomaly_detection/Monte-Carlo-EM-for-Time-Series-Anomaly-Detection-demo-notebook.ipynb
https://github.com/Gabsha/ssbr
Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks
https://github.com/George730/E-ResGAT
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/GiorgioMorales/PredictionIntervals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.