https://github.com/samet-akcay/ganomaly
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
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/samet-akcay/skip-ganomaly
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
https://github.com/samuelmat19/GLOW-tf2
https://github.com/santhoshkelathodi/CNN-VAE
Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE
https://github.com/santhoshkelathodi/CNN-VAE-based-Trajectory-Classification-and-Anomaly-Detection
Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE
https://github.com/santiagxf/ContrastiveLearning
https://github.com/sarus-tech/tf2-published-models
https://github.com/sayakpaul/Generalized-ODIN-TF
https://github.com/seanXYZ/SegDecNet
Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
https://github.com/selimseker/logogram-language-generator
https://github.com/seominseok0429/Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch
https://github.com/septmars/DL
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
https://github.com/sergts/botnet-traffic-analysis
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
https://github.com/seungjunlee96/AnoGAN-pytorch
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
https://github.com/seungmi-oh/AD-CLSCNFs
https://github.com/seymayucer/VAEs
https://github.com/sgamezrdo/anomaly-aoutoencoding
https://github.com/shalder/knowledge_graph
https://github.com/sharmi1206/featal-ecg-anomaly-detection
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
https://github.com/shellysheynin/A-Hierarchical-Transformation-Discriminating-Generative-Model-for-Few-Shot-Anomaly-Detection
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection
https://github.com/shellysheynin/HTDG-model
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection
https://github.com/shenweichen/GraphEmbedding
https://github.com/shib0li/VAE-PyTorch
https://github.com/shib0li/VAE-torch
https://github.com/shijianjian/Adverserial-Dual-AutoEncoder
https://github.com/shinshoji01/Style-Restricted_GAN
https://github.com/shivakanthsujit/vae-pytorch
https://github.com/shivam-131/CLA
Continuous online sequence learning with an unsupervised neural network model
https://github.com/simonwestberg/DD2412-Glow
https://github.com/simonwestberg/Glow
https://github.com/smallcowbaby/OmniAnomaly
https://github.com/smiler96/GCPF
https://github.com/smiler96/PFM-and-PEFM-for-Image-Anomaly-Detection-and-Segmentation
https://github.com/sooonwoo/RotNet-OOD
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/sooooner/MNIST-VAE-with-tensorflow2
https://github.com/sparvangada/capstone_project
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.