 By learning to replicate the most salient features in the training data under some of the constraints described previously, the model is encouraged to learn how to precisely reproduce the most frequent characteristics of the observations.When facing anomalies, the model should worsen its reconstruction performance.In most cases, only data with normal instances are used to train the autoencoder; in others, the frequency of anomalies is so small compared to the whole population of observations, that its contribution to the representation learnt by the model could be ignored.