Background: I recently understood on a deeper level the importance of data augmentation when training convolutional neural networks after seeing this excellent talk by Geoffrey Hinton.
He explains that current generation convolutional neural networks are not able to generalize the frame of reference of the object under test, making it hard for a network to truly understand that mirrored images of an object are the same.
Some research has gone into trying to remedy this. Here is one of the many many examples. I think this helps to establish how critical data augmentation is today when training convolutional neural networks.
Data augmentation techniques are rarely benchmarked against each other. Hence:
What are some papers where the practitioners reported exceptionally better performance?
What are some data augmentation techniques that you have found useful?