R Deep Learning Cookbook

Setting up denoising autoencoders

Denoising autoencoders are a special kind of autoencoder with a focus on extracting robust features from the input dataset. Denoising autoencoders are similar to the previous model except with a major difference that the data is corrupted before training the network. Different approaches for corruption can be used such as masking, which induces random error into the data.