> "Ideally, researchers and machine learning practitioners aim at comparing models with respect to their ability of generalizing to unseen data. With a growing

> "Ideally, researchers and machine learning practitioners aim at comparing models with respect to their ability of generalizing to unseen data. With a growing number of duplicates (in test data), however, we run the risk of comparing them in terms of their capability of memorizing the training data, which increases with model capacity. This is especially problematic when the difference between the error rates of different models is as small as it is nowadays, i.e., sometimes just one or two percent points."