  Lottery Ticket Hypothesis raises keen attention to identifying sparse
trainable subnetworks or winning tickets, at the initialization (or early
stage) of training, which can be trained in isolation to achieve similar or
even better performance compared to the full models. Despite many efforts being
made, the most effective method to identify such winning tickets is still
Iterative Magnitude-based Pruning (IMP), which is computationally expensive and
has to be run thoroughly for every different network. A natural question that
comes in is: can we "transform" the winning ticket found in one network to
another with a different architecture, yielding a winning ticket for the latter
at the beginning, without re-doing the expensive IMP? Answering this question
is not only practically relevant for efficient "once-for-all" winning ticket
finding, but also theoretically appealing for uncovering inherently scalable
sparse patterns in networks. We conduct extensive experiments on CIFAR-10 and
ImageNet, and propose a variety of strategies to tweak the winning tickets
found from different networks of the same model family (e.g., ResNets). Based
on these results, we articulate the Elastic Lottery Ticket Hypothesis (E-LTH):
by mindfully replicating (or dropping) and re-ordering layers for one network,
its corresponding winning ticket could be stretched (or squeezed) into a
subnetwork for another deeper (or shallower) network from the same family,
whose performance is nearly as competitive as the latter's winning ticket
directly found by IMP. We have also thoroughly compared E-LTH with
pruning-at-initialization and dynamic sparse training methods, and discuss the
generalizability of E-LTH to different model families, layer types, and even
across datasets. Our codes are publicly available at
https://github.com/VITA-Group/ElasticLTH.
