MotherNets speed up deep ensemble learning with shared costs.
problem Training deep neural network ensembles is computationally expensive and time-consuming.
method MotherNets train a single or small set of MotherNets to share data movement and computation costs across ensembles, generating target ensembles by transferring function from MotherNets.
result MotherNets provide a new Pareto frontier for accuracy vs. training cost, scaling improvements as ensemble size increases.