An autoencoder is a type of artificial neural network used to learn efficient data (codings) in an unsupervised manner. The aim of an auto encoder is to learn a representation (encoding) for a set of data,
denoising autoencoders is typically a type of autoencoders that trained to ignore “noise’’ in corrupted input samples.
In this code we represent to you a denoising autoencoder with a single hidden layer feed forward networks trained by Extreme learning machine.
This algorithm allows training and testing of any dataset with the user defined parameters and shows the main results of both of them.
in this version noise is added randomly by frames(blocks of data) , if you need any information or help concerning your deep learning projects contact me via: firstname.lastname@example.org
the referances that i used to create the code are available in this link:
BERGHOUT Tarek (2019). Denoising autoencoders (https://www.mathworks.com/matlabcentral/fileexchange/71115-denoising-autoencoders), MATLAB Central File Exchange. Retrieved .
some coments are added
a new version that trains an autoencoders by adding random samples of noise in each frame (block of data) .
a new illustration image is description notes Note were added