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

Top-Down Networks: A coarse-to-fine reimagination of CNNs