  Recent approaches to efficiently ensemble neural networks have shown that
strong robustness and uncertainty performance can be achieved with a negligible
gain in parameters over the original network. However, these methods still
require multiple forward passes for prediction, leading to a significant
computational cost. In this work, we show a surprising result: the benefits of
using multiple predictions can be achieved `for free' under a single model's
forward pass. In particular, we show that, using a multi-input multi-output
(MIMO) configuration, one can utilize a single model's capacity to train
multiple subnetworks that independently learn the task at hand. By ensembling
the predictions made by the subnetworks, we improve model robustness without
increasing compute. We observe a significant improvement in negative
log-likelihood, accuracy, and calibration error on CIFAR10, CIFAR100, ImageNet,
and their out-of-distribution variants compared to previous methods.
