Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey

A vast array of Deep Learning (DL) approaches have been proven successful in many different learning tasks in recent years. One of the key strength of DL models is their ability to automatically learn a representation of the features composing a data set. Deep Neural Networks (DNNs) represent the foremost and widely spread class of DL models. Broadly speaking, DNNs are sub-symbolic ML approaches that are very good at extracting the useful information contained in large data sets. One of the advantages of DL techniques is that, in general, they do not rely on stringent assumptions on the distribution of the underlying data and on the function to be learned or approximated. This allows them to be applied in many different areas with very good results, without significant changes to the DNNs' structure and training algorithm. However, there are contexts where purely data-driven models are not an ideal fit, for example when scarce data of good quality and very difficult learning tasks. In such situations, a great boost in the performance of neural networks (and ML models in general) can be obtained through the exploitation of domain knowledge, e.g.