Therefore, representation learning is a very active area of research [33] with a wide range of applications ranging from neuroscience [27], molecular biology [28], bioinformatics [12]or image analysis [21].

DNNs get more accurate as the size and coverage of training data increase [17]. While investing in high-quality and large-scale labeled data is one path, another is utilizing prior knowledge - concisely referred to as'rules': reasoning heuristics, equations, associative logic, constraints or blacklists.