Parametric Rectified Power Sigmoid Units: Learning Nonlinear Neural Transfer Analytical Forms

TL;DR

This paper introduces a new class of parametric nonlinear activation functions called rectified power sigmoid units, which jointly learn shape parameters alongside convolutional weights, enhancing neural network flexibility and performance.

Contribution

It proposes a novel parametric activation function class that combines sigmoid and ReLU advantages, with learnable parameters for improved neural network modeling.

Findings

Outperforms traditional activations in shallow and deep networks

Enables learning of complex nonlinear shapes

Offers a wide range of activation functions through parameter tuning

Abstract

The paper proposes representation functionals in a dual paradigm where learning jointly concerns both linear convolutional weights and parametric forms of nonlinear activation functions. The nonlinear forms proposed for performing the functional representation are associated with a new class of parametric neural transfer functions called rectified power sigmoid units. This class is constructed to integrate both advantages of sigmoid and rectified linear unit functions, in addition with rejecting the drawbacks of these functions. Moreover, the analytic form of this new neural class involves scale, shift and shape parameters so as to obtain a wide range of activation shapes, including the standard rectified linear unit as a limit case. Parameters of this neural transfer class are considered as learnable for the sake of discovering the complex shapes that can contribute in solving machine learning issues. Performance achieved by the joint learning of convolutional and rectified power sigmoid learnable parameters are shown outstanding in both shallow and deep learning frameworks. This class opens new prospects with respect to machine learning in the sense that learnable parameters are not only attached to linear transformations, but also to suitable nonlinear operators.