Skip to content

honzabim/AnomalyDetection.jl

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Anomaly Detection

Implementation of various generative neural network models for anomaly detection in Julia, using the Flux framework.

Models implemented:

acronym name paper
AE Autoencoder Vincent, Pascal, et al. "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion." Journal of Machine Learning Research 11.Dec (2010): 3371-3408. link
VAE Variational Autoencoder Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013). link
sVAE symetric Variational Autoencoder Pu, Yunchen, et al. "Symmetric variational autoencoder and connections to adversarial learning." arXiv preprint arXiv:1709.01846 (2017). link
GAN Generative Adversarial Network Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014. link
fmGAN GAN with feature-matching loss Salimans, Tim, et al. "Improved techniques for training gans." Advances in Neural Information Processing Systems. 2016. link

Experiments:

Experiments are executed on the Loda (Lightweight on-line detector of anomalies) datasets that can be downloaded here. Tha sampling method is based on this paper. After downloading the datasets, you can create your own using the experiments/prepare_data.jl function.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Julia 100.0%