With data augmentation, we're effectively injecting additional information about what sorts of transformations of the data the model should be insensitive to.

With data augmentation, we're effectively injecting additional information about what sorts of transformations of the data the model should be insensitive to. The additional information comes from our (hopefully) well-informed human decisions about how to augment the data. By doing this, we can reduce the tendency for the model to pick up dependencies on patterns that are useful in the context of the (very small) training dataset, but which don't work well on new data that isn't in the training set.