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arXiv · 2104.09125

SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization

Amir Hertz·Or Perel·Raja Giryes·Olga Sorkine-Hornung·Daniel Cohen-Or
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Abstract

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.

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Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or. 2021-04-19. SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization. https://arxiv.org/abs/2104.09125

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