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.

翻译：本文提出两种新型集成方法，用于降低图像分类的数据与计算成本。这些方法可应用于任意分类器组合，且无需额外训练。第一种方法通过仅在模型对低分辨率像素化版本分类置信度较低时才分析全尺寸图像，从而降低数据使用量。将其应用于本研究中最优分类器时，在MNIST上数据使用量减少61.2%，KMNIST减少69.6%，FashionMNIST减少56.3%，SVHN减少84.6%，ImageNet减少40.6%，ImageNet-V2减少27.6%，且准确率降幅均低于5%。然而，对于CIFAR-10数据集，像素化数据信息量不足，集成方法反而增加数据使用量并降低准确率。第二种方法通过仅在简单模型分类置信度较低时使用复杂模型，从而降低计算成本。计算成本在MNIST上减少82.1%，KMNIST减少47.6%，FashionMNIST减少72.3%，SVHN减少86.9%，ImageNet减少89.2%，ImageNet-V2减少81.5%，准确率降幅均低于5%；而在CIFAR-10上对应改进幅度较小，仅为13.5%。当计算成本无约束时，针对每个观测选择置信度最高模型的投影结果，可将ImageNet验证准确率从79.3%提升至81.0%，ImageNet-V2从67.5%提升至69.4%。