There has been recent interest in improving performance of simple models for
multiple reasons such as interpretability, robust learning from small data,
deployment in memory constrained settings as well as environmental
considerations. In this paper, we propose a novel method SRatio that can
utilize information from high performing complex models (viz. deep neural
networks, boosted trees, random forests) to reweight a training dataset for a
potentially low performing simple model of much lower complexity such as a
decision tree or a shallow network enhancing its performance. Our method also
leverages the per sample hardness estimate of the simple model which is not the
case with the prior works which primarily consider the complex model's
confidences/predictions and is thus conceptually novel. Moreover, we generalize
and formalize the concept of attaching probes to intermediate layers of a
neural network to other commonly used classifiers and incorporate this into our
method. The benefit of these contributions is witnessed in the experiments
where on 6 UCI datasets and CIFAR-10 we outperform competitors in a majority
(16 out of 27) of the cases and tie for best performance in the remaining
cases. In fact, in a couple of cases, we even approach the complex model's
performance. We also conduct further experiments to validate assertions and
intuitively understand why our method works. Theoretically, we motivate our
approach by showing that the weighted loss minimized by simple models using our
weighting upper bounds the loss of the complex model.

本文提出了一种新方法 SRatio，可利用复杂模型（如深度神经网络、增强树、随机森林）的信息对训练数据集进行重新加权，以增强性能较低的简单模型，同时整合中间层探针的概念到我们的方法中，实验结果表明在 6 个 UCI 数据集和 CIFAR-10 中，我们在大多数（27 个案例中有 16 个）情况下都超过竞争对手，并在其余情况下达到最佳表现。此外，我们还进行了进一步的实验，以验证断言并直观理解为什么我们的方法有效。