Framework for the Interactive Learning of Artificial Neural Networks

We propose a framework for interactive learning of artificial neural networks. In this paper we study interaction during training of visualizable supervised tasks. If activity of a hidden node in a network is visualized similar way as are the network outputs, a human observer might deduce the effect of this particular node on the resulting output. We allow human to interfere with the learning process of the network, thus he or she can improve the learning performance by incorporating his or her lifelong experience. This interaction is similar to the process of teaching children, where the teacher observes their responses to questions and guides the process of learning. Several methods of interaction with neural network training are described and demonstrated in the paper.