Denoising Autoencoder (DAE)

In this autoencoder, the network cannot simply copy the input to its output because the input also contains random noise. On DAEs, we are producing it to reduce the noise and result in meaningful data within it. In this case, the algorithm forces the hidden layer to learn more robust features so that the output is a more refined version of the noisy input.