Learning Curves for Analysis of Deep Networks
Derek Hoiem,u00a0Tanmay Gupta,u00a0Zhizhong Li,u00a0Michal Shlapentokh-Rothman
Learning curves model a classifieru2019s 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.
