The reconstruction of output from input does not always guarantee the desired output, and can sometimes end up in simply copying the input. To prevent such a situation, in [134], a different strategy has been proposed. In that proposed architecture, rather than putting some constraints in the representation of the input data, the reconstruction criteria is built, based on cleaning the partially corrupted input.
"A good representation is one that can be obtained robustly from a corrupted input and that will be useful for recovering the corresponding clean input."
A denoising autoencoder is a type of autoencoder which takes corrupted data as input, and the model is trained to predict the original, clean, and uncorrupted data as its output. In this section, we will explain the basic idea behind designing a denoising autoencoder.
