Should the feature space formula_23 have lower dimensionality than the input space formula_24, the feature vector formula_25 can be regarded as a compressed representation of the input formula_26. This is the case of "undercomplete" autoencoders. If the hidden layers are larger than ("overcomplete autoencoders)", or equal to, the input layer, or the hidden units are given enough capacity, an autoencoder can potentially learn the identity function and become useless. However, experimental results have shown that autoencoders might still learn useful features in these cases. In the ideal setting, one should be able to tailor the code dimension and the model capacity on the basis of the complexity of the data distribution to be modeled. One way to do so is to exploit the model variants known as Regularized Autoencoders.