How do we know a neural network test accuracy is good enough when results vary with different runs?
So, the question is about how to report test accuracy, etc, when you see variation over executions.
As @Nikos M. has eluded to, you typically train and test the model at least 3 times and then report the average test evaluation metrics (along with the standard deviation to show the level of variation). This is why you are seeing 'discrete' values reported in papers, this is because they are averaged over x train/test runs.