  Deep Learning (DL) models proved themselves to perform extremely well on a
wide variety of learning tasks, as they can learn useful patterns from large
data sets. However, purely data-driven models might struggle when very
difficult functions need to be learned or when there is not enough available
training data. Fortunately, in many domains prior information can be retrieved
and used to boost the performance of DL models. This paper presents a first
survey of the approaches devised to integrate domain knowledge, expressed in
the form of constraints, in DL learning models to improve their performance, in
particular targeting deep neural networks. We identify five (non-mutually
exclusive) categories that encompass the main approaches to inject domain
knowledge: 1) acting on the features space, 2) modifications to the hypothesis
space, 3) data augmentation, 4) regularization schemes, 5) constrained
learning.
