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Deep Learning (DL) models proved themselves to perform extremely well on awide variety of learning tasks, as they can learn useful patterns from largedata sets. However, purely data-driven models might struggle when verydifficult functions need to be learned or when there is not enough availabletraining data. Fortunately, in many domains prior information can be retrievedand used to boost the performance of DL models. This paper presents a firstsurvey of the approaches devised to integrate domain knowledge, expressed inthe form of constraints, in DL learning models to improve their performance, inparticular targeting deep neural networks. We identify five (non-mutuallyexclusive) categories that encompass the main approaches to inject domainknowledge: 1) acting on the features space, 2) modifications to the hypothesisspace, 3) data augmentation, 4) regularization schemes, 5) constrainedlearning.

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