 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.