is_robust

Detect whether an autoencoder is robust

Implementation of several unsupervised neural networks,
from building their architecture to their training and evaluation. Available
networks are auto-encoders including their main variants: sparse, contractive,
denoising, robust and variational, as described in Charte et al. (2018)
<doi:10.1016/j.inffus.2017.12.007>.

David Charte

ruta

Implementation of Unsupervised Neural Architectures

Francisco Charte

Francisco Herrera

is_robust function

<dl><dt>learner</dt>
<dd>A <code>"ruta_autoencoder"</code> object</dd></dl>

Arguments

Detect whether an autoencoder is robust — is_robust

<dl>

<dt>learner</dt>
<dd>A <code>"ruta_autoencoder"</code> object</dd>

</dl>

is_robust: Detect whether an autoencoder is robust

Description

Usage

Value

Arguments

See Also