  Pruning methods can considerably reduce the size of artificial neural
networks without harming their performance. In some cases, they can even
uncover sub-networks that, when trained in isolation, match or surpass the test
accuracy of their dense counterparts. Here we study the inductive bias that
pruning imprints in such "winning lottery tickets". Focusing on visual tasks,
we analyze the architecture resulting from iterative magnitude pruning of a
simple fully connected network (FCN). We show that the surviving node
connectivity is local in input space, and organized in patterns reminiscent of
the ones found in convolutional networks (CNN). We investigate the role played
by data and tasks in shaping the architecture of pruned sub-networks. Our
results show that the winning lottery tickets of FCNs display the key features
of CNNs. The ability of such automatic network-simplifying procedure to recover
the key features "hand-crafted" in the design of CNNs suggests interesting
applications to other datasets and tasks, in order to discover new and
efficient architectural inductive biases.
