Before revision:  We design a series of toy learning problems to investigate the conditions under which such data augmentation is helpful. We show that augmenting with training examples on which the weak feature fails ("counterexamples") does succeed in preventing the model from relying on the weak feature, but often does not succeed in encouraging the model to use the stronger feature in general. We also find in many cases that the number of counterexamples needed to reach a given error rate is independent of the amount of training data, and that this type of data augmentation becomes less effective as the target strong feature becomes harder to learn .
After revision:  We design a series of toy learning problems to test the hypothesis that data augmentation leads models to unlearn weaker heuristics, but not to learn stronger features in their place. We find partial support for this hypothesis: Data augmentation often hurts before it helps, and it is less effective when the preferred strong feature is much more difficult to extract than the competing weak feature .