Comparing feedforward neural networks using independent component analysis on hidden units

Neural networks are widely used for classification and regression tasks, but they do not always perform well, nor explicitly inform us of the rationale for their predictions. In this study we propose a novel method of comparing a pair of different feedforward neural networks, which draws on independent components obtained by independent component analysis (ICA) on the hidden layers of these networks. It can compare different feedforward neural networks even when they have different structures, as well as feedforward neural networks that learned partially different datasets, yielding insights into their functionality or performance. We evaluate the proposed method by conducting three experiments with feedforward neural networks that have one hidden layer, and verify whether a pair of feedforward neural networks can be compared by the proposed method when the numbers of hidden units in the layer are different, when the datasets are partially different, and when activation functions are different. The results show that similar independent components are extracted from two feedforward neural networks, even when the three circumstances above are different. Our experiments also reveal that mere comparison of weights or activations does not lead to identifying similar relationships. Through the extraction of independent components, the proposed method can assess whether the internal processing of one neural network resembles that of another. This approach has the potential to help understand the performance of neural networks.