Image classification is a fundamental building block for a majority of
computer vision applications. With the growing popularity and capacity of
machine learning models, people can easily access trained image classifiers as
a service online or offline. However, model use comes with a cost and
classifiers of higher capacity usually incur higher inference costs. To harness
the respective strengths of different classifiers, we propose a principled
approach, OCCAM, to compute the best classifier assignment strategy over image
classification queries (termed as the optimal model portfolio) so that the
aggregated accuracy is maximized, under user-specified cost budgets. Our
approach uses an unbiased and low-variance accuracy estimator and effectively
computes the optimal solution by solving an integer linear programming problem.
On a variety of real-world datasets, OCCAM achieves 40% cost reduction with
little to no accuracy drop.

OCCAM 是一种基于整数线性规划问题的原则方法，用于计算图像分类查询的最佳分类器分配策略，以在用户指定的成本预算下最大化聚合准确性。在各种真实数据集上，OCCAM 实现了 40% 的成本降低，同时准确性几乎没有下降。