We have discussed how deep learning is fundamentally based on gray-box models that learn how to associate input patterns to specific classification/regression outcomes. All the processing pipeline that is often employed to prepare the data for specific detections is absorbed by the complexity of the neural architecture. However, the price to pay for high accuracies is a proportionally large number of training samples. State-of-the-art visual networks are trained with millions of images and, obviously, each of them must be properly labeled. Even if there are many free datasets that can be employed to train several models, many specific scenarios need hard preparatory work that sometimes is very difficult to achieve.
Luckily, deep neural architectures are hierarchical models that learn in a structured way. As we have seen in the examples of deep convolutional networks, the first layers become more and more sensitive to detect low-level features, while the higher ones concentrate...