  The recently proposed Lottery Ticket Hypothesis of Frankle and Carbin (2019)
suggests that the performance of over-parameterized deep networks is due to the
random initialization seeding the network with a small fraction of favorable
weights. These weights retain their dominant status throughout training -- in a
very real sense, this sub-network "won the lottery" during initialization. The
authors find sub-networks via unstructured magnitude pruning with 85-95% of
parameters removed that train to the same accuracy as the original network at a
similar speed, which they call winning tickets. In this paper, we extend the
Lottery Ticket Hypothesis to a variety of transfer learning tasks. We show that
sparse sub-networks with approximately 90-95% of weights removed achieve (and
often exceed) the accuracy of the original dense network in several realistic
settings. We experimentally validate this by transferring the sparse
representation found via pruning on CIFAR-10 to SmallNORB and FashionMNIST for
object recognition tasks.
