  Ensembling is a simple and popular technique for boosting evaluation
performance by training multiple models (e.g., with different initializations)
and aggregating their predictions. This approach is commonly reserved for the
largest models, as it is commonly held that increasing the model size provides
a more substantial reduction in error than ensembling smaller models. However,
we show results from experiments on CIFAR-10 and ImageNet that ensembles can
outperform single models with both higher accuracy and requiring fewer total
FLOPs to compute, even when those individual models' weights and
hyperparameters are highly optimized. Furthermore, this gap in improvement
widens as models become large. This presents an interesting observation that
output diversity in ensembling can often be more efficient than training larger
models, especially when the models approach the size of what their dataset can
foster. Instead of using the common practice of tuning a single large model,
one can use ensembles as a more flexible trade-off between a model's inference
speed and accuracy. This also potentially eases hardware design, e.g., an
easier way to parallelize the model across multiple workers for real-time or
distributed inference.
