Data augmentation is a technique where we apply transformations to an image and use both the original image and the transformed images to train on. Imagine we had a training set with a cat in it:

If we were to apply a horizontal flip to this image, we'd get something that looks like this:

This is exactly the same image, of course, but we can use both the original and transformation as training examples. This isn't quite as good as two separate cats in our training set; however, it does allow us to teach the computer that a cat is a cat regardless of the direction it's facing.

In practice, we can do a lot more than just a horizontal flip. We can vertically flip, when it makes sense, shift, and randomly rotate images as well. This allows us to artificially amplify our dataset and make it seem bigger than it is. Of course you can only push this so far, but it's a very powerful tool in the fight against overfitting when little data exists.