Computer Vision research today is focusing more and more on deep learning, because it is the machine learning paradigm that produces the best results. The main feature is that we can train an end-to-end network both to extract features from the data and to create models able to exploit them to solve certain tasks. In this way there is no need to manually extrapolate the right characteristics from the data to pass to the final model, thus overcoming the problems of shallow approaches and guaranteeing results with a much lower error rate. The main limitation is that deep networks have many parameters and therefore, in order to be trained, they need a large amount of labelled data, which is not always available.