Improving image classifiers for small datasets by learning rate adaptations
This work addresses the challenge of reliable machine reasoning for small datasets, such as in diagnostic imaging, though it is incremental as it combines established techniques.
The paper tackles the problem of improving image classifier performance on small datasets by dynamically tuning the learning rate, achieving a two-fold to ten-fold speedup in nearing state-of-the-art accuracy across different model architectures.
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.