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/chandu-97/BayesByBackprop
https://github.com/chaoyuhao/AnomalyR1
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection
https://github.com/cheng0719/E-GraphSAGE_NIDS
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/chengyui/nuwats
https://github.com/chickenbestlover/RNN-Time-series-Anomaly-Detection
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
https://github.com/chrischute/glow
https://github.com/christabella/bruno
https://github.com/chrvt/denoising-normalizing-flow
https://github.com/chunjingxiao/diffad
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
https://github.com/ci-ber/autoddpm
https://github.com/cianeastwood/qedr
https://github.com/cics-nd/predictive-cvs
https://github.com/cinout/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/ciol-researchlab/SupplyGraph
https://github.com/ciol-sust/supplygraph
https://github.com/clementchadebec/benchmark_VAE
https://github.com/cmougan/IsolationForest
https://github.com/code-implementation1/Code6/tree/main/PaDiM
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
https://github.com/code-implementation1/Code6/tree/main/PatchCore
https://github.com/conan7882/adversarial-autoencoders
https://github.com/cool-xuan/Imbalanced_SAM/tree/main/imbsam_mindspore
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
https://github.com/cptpirx/aursad
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset
https://github.com/crimama/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/cross32768/AVID-Adversarial-Visual-Irregularity-Detection
https://github.com/crystal02146/AnoGAN-Keras
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/cure-lab/DLinear
https://github.com/cure-lab/ltsf-linear
https://github.com/cvjena/4DPADCurvAnalysis
Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection
https://github.com/cvlab-unibo/crossmodal-feature-mapping
Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
https://github.com/da-head0/cctv-anomaly-detection
https://github.com/dabsdamoon/MNIST-Auxiliary-Decoder
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/damo-di-ml/icml2022-fedformer
https://github.com/damo-di-ml/neurips2023-one-fits-all
https://github.com/danbochman/Efficient-GAN-Based-Anomaly-Detection-TF2
https://github.com/danbochman/GANomaly-TF2
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.