Image Classification Using Deep Learning and Prior Knowledge

Deep learning has been very successful on image classification tasks in the past few years, because it allows to develop end-to-end solutions, taking as input the raw images in form of a grid of pixels and returning the class assignments. Semantic Based Regularization is used in this paper as a general and novel way to integrate prior knowledge into deep learning. Semantic Based Regularization takes as input the prior knowledge, expressed as a collection of first-order logic clauses (FOL), where each task to be learned corresponds to a predicate in the knowledge base. Then, it translates the knowledge into a set of constraints which can be either integrated into the learning process or used in a collective classification step during the test phase. The integration of the domain knowledge during the train or test phase is realized via the same backpropagation schema that runs over the expression trees of the grounded FOL clauses. The methodology can be applied on top of any learner and the experimental results on CIFAR-10 show how the integration of the prior knowledge boosts the accuracy of many different deep architectures.