Learning Curves for Analysis of Deep Networks
Abstract
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.
Cite
Text
Hoiem et al. "Learning Curves for Analysis of Deep Networks." International Conference on Machine Learning, 2021.Markdown
[Hoiem et al. "Learning Curves for Analysis of Deep Networks." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/hoiem2021icml-learning/)BibTeX
@inproceedings{hoiem2021icml-learning,
title = {{Learning Curves for Analysis of Deep Networks}},
author = {Hoiem, Derek and Gupta, Tanmay and Li, Zhizhong and Shlapentokh-Rothman, Michal},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {4287-4296},
volume = {139},
url = {https://mlanthology.org/icml/2021/hoiem2021icml-learning/}
}