Breakdown of Key Accuracy Improvements
It is important to note that implementing a model directly from a paper is an iterative process that circles between coding, training, bug fixing and adapting the configuration until we match the accuracies reported on the paper. Quite often it also involves simplifying the training recipe or enhancing it with more recent methodologies. It is definitely not a linear process where incremental accuracy improvements are achieved by improving a single direction at a time but instead involves exploring different hypothesis, making incremental improvements in different aspects and doing a lot of backtracking. 
With that in mind, below we try to summarize the optimizations that affected our accuracy the most. We did this by grouping together the various experiments in 4 main groups and attributing the experiment improvements to the closest match. Note that the Y-axis of the graph starts from 18 instead from 0 to make the difference between optimizations more visible: