  The success of deep ensembles on improving predictive performance,
uncertainty estimation, and out-of-distribution robustness has been extensively
studied in the machine learning literature. Albeit the promising results,
naively training multiple deep neural networks and combining their predictions
at inference leads to prohibitive computational costs and memory requirements.
Recently proposed efficient ensemble approaches reach the performance of the
traditional deep ensembles with significantly lower costs. However, the
training resources required by these approaches are still at least the same as
training a single dense model. In this work, we draw a unique connection
between sparse neural network training and deep ensembles, yielding a novel
efficient ensemble learning framework called FreeTickets. Instead of training
multiple dense networks and averaging them, we directly train sparse
subnetworks from scratch and extract diverse yet accurate subnetworks during
this efficient, sparse-to-sparse training. Our framework, FreeTickets, is
defined as the ensemble of these relatively cheap sparse subnetworks. Despite
being an ensemble method, FreeTickets has even fewer parameters and training
FLOPs than a single dense model. This seemingly counter-intuitive outcome is
due to the ultra training/inference efficiency of dynamic sparse training.
FreeTickets surpasses the dense baseline in all the following criteria:
prediction accuracy, uncertainty estimation, out-of-distribution (OoD)
robustness, as well as efficiency for both training and inference.
Impressively, FreeTickets outperforms the naive deep ensemble with ResNet50 on
ImageNet using around only 1/5 of the training FLOPs required by the latter. We
have released our source code at https://github.com/VITA-Group/FreeTickets.
