  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 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.
