Comparison of Combinations of Data Augmentation Methods and Transfer Learning Strategies in Image Classification Used in Convolution Deep Neural Networks
Timofey A. Korzhebin, Alexey D. Egorov
Abstract
Several studies have already made a comparison of either Data Augmentation methods or Transfer learning strategies in Convolution Deep Neural Networks for Image Classification; however, comparison of combinations of Data Augmentation methods and Transfer learning strategies remains to be accomplished. Combination of Data Augmentation methods with the highest-performing results and Transfer learning strategy with the highest-performing results does not achieve top-performing results in total as well. We make the comparison of four Data Augmentation methods, the comparison of four Transfer learning strategies, used on five different image classification models and the comparison of all combinations of them. We use small dataset consists of 40 images for training and finetuning and accuracy as metric. Our research shows that the performance results of a model with combinations of methods and strategies cannot be expected from simple comparisons of parts of this combination.