Taming the ReLU with Parallel Dither in a Deep Neural Network

Rectified Linear Units (ReLU) seem to have displaced traditional ‘smooth’ nonlinearities as activationfunction-du-jour in many – but not all deep neural network (DNN) applications. However, nobody seems to know why. In this article, we argue that ReLU are useful because they are ideal demodulators – this helps them perform fast abstract learning. However, this fast learning comes at the expense of serious nonlinear distortion products decoy features. We show that Parallel Dither acts to suppress the decoy features, preventing overfitting and leaving the true features cleanly demodulated for rapid, reliable learning.