Our paper introduces an efficient combination of established techniques to improve classifier performance, in terms of accuracy and training time. We achieve two-fold to ten-fold speedup in nearing state of the art accuracy, over different model architectures, by dynamically tuning the learning rate. We find it especially beneficial in the case of a small dataset, where reliability of machine reasoning is lower. We validate our approach by comparing our method versus vanilla training on CIFAR-10. We also demonstrate its practical viability by implementing on an unbalanced corpus of diagnostic images.
@article{arxiv.1903.10726,
title = {Improving image classifiers for small datasets by learning rate adaptations},
author = {Sourav Mishra and Toshihiko Yamasaki and Hideaki Imaizumi},
journal= {arXiv preprint arXiv:1903.10726},
year = {2019}
}