Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Graph Fraud Detection Papers | 1,148 | 4 months ago | ||||||||
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources | ||||||||||
Mantranet Pytorch | 101 | 5 months ago | 3 | Jupyter Notebook | ||||||
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch | ||||||||||
Cyber Security | 89 | 2 years ago | 1 | Jupyter Notebook | ||||||
Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities | ||||||||||
Deep Learning For Anomaly Detection A Survey | 87 | 5 years ago | TeX | |||||||
This paper is continuously updated with deep anomaly detection methods and their applications | ||||||||||
Practicalmachinelearning | 79 | 3 months ago | bsd-3-clause | Jupyter Notebook | ||||||
A curated collection of machine learning resources, including notebooks, code, and books, all of which are either free or open-source | ||||||||||
Deepai | 74 | 5 years ago | 1 | gpl-3.0 | Jupyter Notebook | |||||
Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch. | ||||||||||
Unsupervised Anomaly Detection With Generative Adversarial Networks | 74 | 7 years ago | 2 | Python | ||||||
Unsupervised Anomaly Detection with Generative Adversarial Networks on MIAS dataset | ||||||||||
Gcn_anomalydetection | 73 | 3 years ago | 3 | Python | ||||||
Code for Deep Anomaly Detection on Attributed Networks (SDM2019) | ||||||||||
Df Ml Anomaly Detection | 69 | 4 years ago | 1 | apache-2.0 | Java | |||||
Streaming Anomaly Detection Solution by using Pub/Sub, Dataflow, BQML & Cloud DLP | ||||||||||
Od Test | 44 | 4 years ago | mit | Python | ||||||
OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch) |