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/Goulustis/One_Class_MNIST
https://github.com/GuHongyang/VAEs
https://github.com/GuansongPang/deep-outlier-detection
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
https://github.com/HSoo-Kim/SpatioTemporal-AutoEncoder
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
https://github.com/Hamid-Jalalzai/Concentration-Bounds-for-the-Empirical-Angular-Measure-with-Statistical-Learning-Applications
Concentration bounds for the empirical angular measure with statistical learning applications
https://github.com/Hannibal046/GridTST
https://github.com/Hashir44/muraxray
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
https://github.com/HolyBayes/pro
https://github.com/Hong-Jeongmin/OC-for-smart-factory
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
https://github.com/ISICV/ManTraNet
https://github.com/IntelLabs/AVUC
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
https://github.com/IntelLabs/TSAD-Evaluator
https://github.com/Jaizxzx/PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/JakobCode/UncertaintyInNeuralNetworks_Resources
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/JerryX1110/Awesome-3D-Anomaly-Detection
The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization
https://github.com/JoegameZhou/PatchCore
https://github.com/JohnnyHopp/PaDiM-EfficientNetV2
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/JuliaWolleb/diffusion-anomaly
https://github.com/June01/tcc_Temporal_Cycle_Consistency_Loss.pytorch
https://github.com/KDD-OpenSource/DeepADoTS
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/KONI-SZ/MSCRED
https://github.com/KarthikKothareddy/pyod
https://github.com/Kaslanarian/SAGOD
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
https://github.com/Kaz-K/unsupervised-brain-abnormality-detection
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection
https://github.com/KevinWangHP/Anomaly-Clustering
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
https://github.com/KiUngSong/Generative-Models
https://github.com/Kismuz/crypto_spread_test
https://github.com/Koukyosyumei/AIJack
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/KurochkinAlexey/TSPrecisionRecall
https://github.com/L0SG/NanoFlow
https://github.com/LSSTDESC/DeblenderVAE
https://github.com/LarryJiang134/Image_manipulation_detection
https://github.com/Lee-JongHyeon/Anomaly-Detection-using-Score-based-Perturbation-Resilience
https://github.com/LeeDoYup/RobustSTL
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.