Papers with Code 아카이브
Anomaly Detection
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- 평가표
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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/polimi-ispl/landmine_detection_autoencoder
Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data
https://github.com/porscheofficial/distribution_and_volume_based_isolation_forest
https://github.com/pps-lab/fl-analysis
https://github.com/pranavjadhav001/tf_padim
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/pridiltal/stray_manuscript
https://github.com/printfer/CS6490_NetworkSecurity_FinalProject
https://github.com/pwc-1/Paper-10/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-8/tree/main/auto
https://github.com/pwc-1/Paper-9/tree/main/1/time_series_transformer
https://github.com/pwc-1/Paper-9/tree/main/1/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pyaf/DenseNet-MURA-PyTorch
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
https://github.com/pylink88/recurrent-autoencoder
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/pytorch/botorch
https://github.com/raghavchalapathy/oc-nn_old
https://github.com/rajkumargithub/densenet.mura
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
https://github.com/regel/loudml
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
https://github.com/rekkles2/Fed_WSVAD
https://github.com/remigenet/TLN
https://github.com/remmarp/PaDiM-TF
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/rforgione/deepwalk
https://github.com/rhychen/Glow
https://github.com/rickyHong/GANomaly-repl
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-Hadoop-distributed-file-system-log
https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-unstructured-Hadoop-distributed-file-system-logs
https://github.com/robeespi/Weakly-Supervised-Malware-Detection
https://github.com/roccojhu/neural_regression_discontinuity
https://github.com/rohban-lab/Knowledge_Distillation_AD
Multiresolution Knowledge Distillation for Anomaly Detection
https://github.com/romanovar/evaluation_MIL
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
https://github.com/rosinality/glow-pytorch
https://github.com/rvorias/ind_knn_ad
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
https://github.com/rvorias/uvHolographics
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
https://github.com/rximg/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/safe-graph/dgfraud-tf2
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
https://github.com/safwankdb/Variational-Auto-Encoder
https://github.com/sakshi-mishra/solar-forecasting-RNN
Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.