DAE are another small variant of the autoencoder. They are mainly used to remove noise from the image, audio, and other inputs. So, when we feed the corrupted input to the DAE, it learns to reconstruct the original uncorrupted input. Now we inspect how DAEs remove the noise.
With a DAE, instead of feeding the raw input to the autoencoder, we corrupt the input by adding some stochastic noise and feed the corrupted input. We know that the encoder learns the representation of the input by keeping only important information and maps the compressed representation to the bottleneck. When the corrupted input is sent to the encoder, while learning the representation of the input encoder will learn that noise is unwanted information and removes its representation. Thus, encoders learn the compact representation of the input without noise by keeping only...