Classless Association Using Neural Networks

Abstract

The goal of this paper is to train a model based on the relation between two instances that represent the same unknown class. This task is inspired by the Symbol Grounding Problem and the association learning between modalities in infants. We propose a novel model called Classless Association that has two parallel Multilayer Perceptrons (MLPs) with a EM-training rule. Moreover, the training relies on matching the output vectors of the MLPs against a statistical distribution as alternative loss function because of the unlabeled data. In addition, the output classification of one network is used as target of the other network, and vice versa for learning the agreement between both unlabeled sample. We generate four classless datasets based on MNIST, where the input is two different instances of the same digit. Furthermore, our classless association model is evaluated against two scenarios: totally supervised and totally unsupervised. In the first scenario, our model reaches a good performance in terms of accuracy and the classless constraint. In the second scenario, our model reaches better results against two clustering algorithms.