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

Understand

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.