Learning curves model a classifier's test error as a function of the number of training samples. Prior works show that learning curves can be used to select model parameters and extrapolate performance. We investigate how to use learning curves to evaluate design choices, such as pretraining, architecture, and data augmentation. We propose a method to robustly estimate learning curves, abstract their parameters into error and data-reliance, and evaluate the effectiveness of different parameterizations. Our experiments exemplify use of learning curves for analysis and yield several interesting observations.

翻译：学习曲线模型 分类器的测试错误, 取决于培训样本的数量。 先前的工程显示, 学习曲线可用于选择模型参数和外推性能 。 我们研究如何使用学习曲线来评价设计选择, 如培训前、 建筑和数据扩增 。 我们提出了一种方法, 以强力估计学习曲线, 将其参数抽象成错误和数据依赖性, 并评估不同参数化的效果 。 我们的实验用学习曲线作为分析的范例, 并得出一些有趣的观察结果 。