  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.
