Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections. (arXiv:2112.04162v2 [cs.CV] CROSS LISTED)

Symmetry is omnipresent in nature and perceived by the visual system of many
species, as it facilitates detecting ecologically important classes of objects
in our environment. Symmetry perception requires abstraction of long-range
spatial dependencies between image regions, and its underlying neural
mechanisms remain elusive. In this paper, we evaluate Deep Neural Network (DNN)
architectures on the task of learning symmetry perception from examples. We
demonstrate that feed-forward DNNs that excel at modelling human performance on
object recognition tasks, are unable …

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