Improving image classifiers for small datasets by learning rate adaptations

Sourav Mishra; Toshihiko Yamasaki; Hideaki Imaizumi

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arXiv · 1903.10726

Improving image classifiers for small datasets by learning rate adaptations

Sourav Mishra·Toshihiko Yamasaki·Hideaki Imaizumi

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Abstract

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.

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Sourav Mishra, Toshihiko Yamasaki, Hideaki Imaizumi. 2019-03-26. Improving image classifiers for small datasets by learning rate adaptations. https://arxiv.org/abs/1903.10726

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