Autoencoders are neural networks designed for feature extraction and selection. They consist of an encoder and decoder. However, an important challenge arises when the hidden layer has more nodes than the input layer. This risk of learning the 'Identity Function' can render the autoencoder ineffective.
Learning the Identity Function implies that the output equals the input. This issue arises when the hidden layer is too large, undermining the feature extraction goal. A remedy is needed to make autoencoders more robust.
Denoising Autoencoders address the identity function problem. They intentionally corrupt the input data by randomly setting some input values to zero. Typically, around 50% of input nodes are affected, though the percentage may vary based on data and input size.
During training, it's vital to calculate the loss by comparing the network's output with the original input data, not the corrupted input. This approach ensures the network learns to extract valuable features instead of reproducing the identity function.