  This paper introduces two new ensemble-based methods to reduce the data and
computation costs of image classification. They can be used with any set of
classifiers and do not require additional training. In the first approach, data
usage is reduced by only analyzing a full-sized image if the model has low
confidence in classifying a low-resolution pixelated version. When applied on
the best performing classifiers considered here, data usage is reduced by 61.2%
on MNIST, 69.6% on KMNIST, 56.3% on FashionMNIST, 84.6% on SVHN, 40.6% on
ImageNet, and 27.6% on ImageNet-V2, all with a less than 5% reduction in
accuracy. However, for CIFAR-10, the pixelated data are not particularly
informative, and the ensemble approach increases data usage while reducing
accuracy. In the second approach, compute costs are reduced by only using a
complex model if a simpler model has low confidence in its classification.
Computation cost is reduced by 82.1% on MNIST, 47.6% on KMNIST, 72.3% on
FashionMNIST, 86.9% on SVHN, 89.2% on ImageNet, and 81.5% on ImageNet-V2, all
with a less than 5% reduction in accuracy; for CIFAR-10 the corresponding
improvements are smaller at 13.5%. When cost is not an object, choosing the
projection from the most confident model for each observation increases
validation accuracy to 81.0% from 79.3% for ImageNet and to 69.4% from 67.5%
for ImageNet-V2.
