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This paper introduces two new ensemble-based methods to reduce the data andcomputation costs of image classification. They can be used with any set ofclassifiers and do not require additional training. In the first approach, datausage is reduced by only analyzing a full-sized image if the model has lowconfidence in classifying a low-resolution pixelated version. When applied onthe 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% onImageNet, and 27.6% on ImageNet-V2, all with a less than 5% reduction inaccuracy. However, for CIFAR-10, the pixelated data are not particularlyinformative, and the ensemble approach increases data usage while reducingaccuracy. In the second approach, compute costs are reduced by only using acomplex 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% onFashionMNIST, 86.9% on SVHN, 89.2% on ImageNet, and 81.5% on ImageNet-V2, allwith a less than 5% reduction in accuracy; for CIFAR-10 the correspondingimprovements are smaller at 13.5%. When cost is not an object, choosing theprojection from the most confident model for each observation increasesvalidation accuracy to 81.0% from 79.3% for ImageNet and to 69.4% from 67.5%for ImageNet-V2.

Problem-dependent attention and effort in neural networks with applications to image resolution and model selection