论文标题
学习曲线用于分析深网络
Learning Curves for Analysis of Deep Networks
论文作者
论文摘要
学习曲线模拟分类器的测试误差与训练样本数量的函数。先前的工作表明，学习曲线可用于选择模型参数并推断性能。我们研究了如何使用学习曲线来评估设计选择，例如训练，架构和数据增强。我们提出了一种方法，可以稳健地估算学习曲线，将其参数抽象为错误和数据依赖性，并评估不同参数化的有效性。我们的实验例证了学习曲线进行分析并产生一些有趣的观察结果。
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.
