PhaseDNN - A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning

Understand

In this paper, we propose a phase shift deep neural network (PhaseDNN) which provides a wideband convergence in approximating a high dimensional function during its training of the network.

• The PhaseDNN utilizes the fact that many DNN achieves convergence in the low frequency range first, thus, a series of moderately-sized of DNNs are constructed and trained in parallel for ranges of higher frequencies.
• With the help of phase shifts in the frequency domain, implemented through a simple phase factor multiplication on the training data, each DNN in the series will be trained to approximate the target function's higher frequency content over a specific range.
• Due to the phase shift, each DNN achieves the speed of convergence as in the low frequency range.