Classless Association using Neural Networks

Federico Raue, Sebastian Palacio, Andreas Dengel, Marcus Liwicki

TL;DR: Learning based on the relation between two instances of the same unknown class

Abstract: In this paper, we propose a model for the classless association between two instances of the same unknown class. This scenario is inspired by the Symbol Grounding Problem and the association learning in infants. Our model has two parallel Multilayer Perceptrons (MLPs) and relies on two components. The first component is a EM-training rule that matches the output vectors of a MLP to a statistical distribution. The second component exploits the output classification of one MLP as target of the another MLP in order to learn the agreement of the unknown class. We generate four classless datasets (based on MNIST) with uniform distribution between the classes. Our model is evaluated against totally supervised and totally unsupervised scenarios. In the first scenario, our model reaches good performance in terms of accuracy and the classless constraint. In the second scenario, our model reaches better results against two clustering algorithms.

Conflicts: dfki.de, uni-kl.de