RESEARCH

COMPUTER VISION

Are Convolutional Networks Inherently Foveated?

December 08, 2021

Abstract

When convolutional layers apply no padding, central pixels have more ways to contribute to the convolution than peripheral pixels. Such discrepancy grows exponentially with the number of layers, leading to implicit foveation of the input pixels. We show that this discrepancy can persist even when padding is applied. In particular, with the commonly-used zero-padding, foveation effects are significantly reduced but not eliminated. We explore how different aspects of convolution arithmetic impact the extent and magnitude of these effects, and elaborate on which alternative padding techniques can mitigate it. Finally, we compare our findings with foveation in human vision, suggesting that both effects possibly have similar nature and implications.

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AUTHORS

Written by

Bilal Alsallakh

David Adkins

Narine Kokhlikyan

Orion Reblitz-Richardson

Vivek Miglani

Publisher

NeurIPS SVRHM Workshop

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