Lack of data represents one of the main problems in the Machine Learning field. The greater part of the algorithms, used for different scopes and goals, needs a huge quantity of data to be trained on. This problem becomes even bigger in the Deep Learning field, because usually, neural networks require more data than classical machine learning algorithms. Strictly connected to this, there is also the problem of class imbalance that can arise for different kinds of situations, above all in classification tasks. With class imbalance, machine learning models will typically over-classify the larger class due to their increased prior probability.