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Many researchers implicitly assume that neural networks learn relations andgeneralise them to new unseen data. It has been shown recently, however, thatthe generalisation of feed-forward networks fails for identity relations.Theproposed solution for this problem is to create an inductive bias withDifferential Rectifier (DR) units. In this work we explore various factors inthe neural network architecture and learning process whether they make adifference to the generalisation on equality detection of Neural Networkswithout and and with DR units in early and mid fusion architectures.  We find in experiments with synthetic data effects of the number of hiddenlayers, the activation function and the data representation. The training setsize in relation to the total possible set of vectors also makes a difference.However, the accuracy never exceeds 61% without DR units at 50% chance level.DR units improve generalisation in all tasks and lead to almost perfect testaccuracy in the Mid Fusion setting. Thus, DR units seem to be a promisingapproach for creating generalisation abilities that standard networks lack.

Factors for the Generalisation of Identity Relations by Neural Networks