A UNIFIED VIEW OF FINDING AND TRANSFORMING WINNING LOTTERY TICKETS

While over-parameterized deep neural networks obtain prominent results on various machine learning tasks, their superfluous parameters usually make model training and inference notoriously inefficient. Lottery Ticket Hypothesis (LTH) addresses this issue from a novel perspective: it articulates that there always exist sparse and admirable subnetworks in a randomly initialized dense network, which can be realized by an iterative pruning strategy. Dual Lottery Ticket Hypothesis (DLTH) further investigates sparse network training from a complementary view. Concretely, it introduces a gradually increased regularization term to transform a dense network to an ultra-light subnetwork without sacrificing learning capacity. After revisiting the success of LTH and DLTH, we unify these two research lines by coupling the stability of iterative pruning and the excellent performance of increased regularization, resulting in two new algorithms (UniLTH and UniDLTH) for finding and transforming winning tickets, respectively. Unlike either LTH without regularization or DLTH which applies regularization across the training, our methods first train the network without any regularization force until the model reaches a certain point (i.e., the validation loss does not decrease for several epochs), and then employ increased regularization for information extrusion and iteratively perform magnitude pruning till the end. We theoretically prove that the early stopping mechanism acts analogously as regularization and can help the optimization trajectory stop at a particularly better point in space than regularization. This not only prevent the parameters from being excessively skewed to the training distribution (over-fitting), but also better stimulate the network potential to obtain more powerful subnetworks. Extensive experiments are conducted to show the superiority of our methods in terms of accuracy and sparsity.