Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands. We present a spatially adaptive progressive encoding (SAPE) scheme for input signals of MLP networks, which enables them to better fit a wide range of frequencies without sacrificing training stability or requiring any domain specific preprocessing. SAPE gradually unmasks signal components with increasing frequencies as a function of time and space. The progressive exposure of frequencies is monitored by a feedback loop throughout the neural optimization process, allowing changes to propagate at different rates among local spatial portions of the signal space. We demonstrate the advantage of SAPE on a variety of domains and applications, including regression of low dimensional signals and images, representation learning of occupancy networks, and a geometric task of mesh transfer between 3D shapes.
@article{arxiv.2104.09125,
title = {SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization},
author = {Amir Hertz and Or Perel and Raja Giryes and Olga Sorkine-Hornung and Daniel Cohen-Or},
journal= {arXiv preprint arXiv:2104.09125},
year = {2021}
}