Learned-norm pooling for deep feedforward and recurrent neural networks

In this paper we propose and investigate a novel nonlinear unit, called L_p unit, for deep neural networks. The proposed L _p unit receives signals from several projections of a subset of units in the layer below and computes a normalized L _p norm. We notice two interesting interpretations of the L_p unit. First, the proposed unit can be understood as a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance, convolutional neural networks (CNN), HMAX models and neocognitrons. Furthermore, the L _p unit is, to a certain degree, similar to the recently proposed maxout unit [13] which achieved the state-of-the-art object recognition results on a number of benchmark datasets. Secondly, we provide a geometrical interpretation of the activation function based on which we argue that the L _p unit is more efficient at representing complex, nonlinear separating boundaries. Each L _p unit defines a superelliptic boundary, with its exact shape defined by the order p. We claim that this makes it possible to model arbitrarily shaped, curved boundaries more efficiently by combining a few L _p units of different orders. This insight justifies the need for learning different orders for each unit in the model. We empirically evaluate the proposed L _p units on a number of datasets and show that multilayer perceptrons (MLP) consisting of the L _p units achieve the state-of-the-art results on a number of benchmark datasets. Furthermore, we evaluate the proposed L _p unit on the recently proposed deep recurrent neural networks (RNN).