We propose a novel adaptive approximation approach for test-time resource-constrained prediction. Given an input instance at test-time, a gating function identifies a prediction model for the input among a collection of models. Our objective is to minimize overall average cost without sacrificing accuracy. We learn gating and prediction models on fully labeled training data by means of a bottom-up strategy. Our novel bottom-up method first trains a high-accuracy complex model. Then a low-complexity gating and prediction model are subsequently learned to adaptively approximate the high-accuracy model in regions where low-cost models are capable of making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.

本文提出了一种新的自适应逼近方法，用于测试时的资源受限预测，通过门控和预测模型在训练数据中学习，实现在低成本模型精度较高的区域适应性逼近高准确度模型，并通过代价约束下的经验损失最小化问题来共同训练门控和预测模型，在多个基准数据集上实现了优于现有技术的高准确度。

预算下的自适应分类预测