Structural Mapping with Identical Elements Neural Network

In training two networks with shared weights on tasks that are analogous, the shared weights tend to encode the high level similarity between the two tasks. In this work, knowledge transfer was studies by investigating the structural mapping with the proposed identical elements neural network which features shared hidden layers. First, two networks were trained simultaneously on structurally analogous tasks. After it converged, cross computation was performed. The result shows that structural mapping between the two tasks can be observed from the activated outputs.