  Biological vision adopts a coarse-to-fine information processing pathway,
from initial visual detection and binding of salient features of a visual
scene, to the enhanced and preferential processing given relevant stimuli. On
the contrary, CNNs employ a fine-to-coarse processing, moving from local,
edge-detecting filters to more global ones extracting abstract representations
of the input. In this paper we reverse the feature extraction part of standard
bottom-up architectures and turn them upside-down: We propose top-down
networks. Our proposed coarse-to-fine pathway, by blurring higher frequency
information and restoring it only at later stages, offers a line of defence
against adversarial attacks that introduce high frequency noise. Moreover,
since we increase image resolution with depth, the high resolution of the
feature map in the final convolutional layer contributes to the explainability
of the network's decision making process. This favors object-driven decisions
over context driven ones, and thus provides better localized class activation
maps. This paper offers empirical evidence for the applicability of the
top-down resolution processing to various existing architectures on multiple
visual tasks.
