[bumby]

Or couldn't they provide data augmentation on the same samples to give the effect of a more diverse (and more populous) training set?

Using the blog's skin cancer example, couldn't the labelled images be augmented by altering the skin tones and adding these new examples to the training set?

It seems to me that some of the anomalous results discussed in the article are actually the result of poor model design or poor pre-processing data choices. We can't just throw anything to any ol' machine learning model and expect it to be magic