<p>The litterature on data augmentation is very very large and very dependent on your kind of applications. 
The first things that come to my mind are the galaxy competition's rotations and Jasper Snoeke's data augmentation.  </p>

<p>But really all papers have their own tricks to get good scores on special datasets for exemples stretching the image to a specific size before cropping it or whatever and this in a very specific order.</p>

<p>More practically to train models on the likes of CIFAR or IMAGENET use random crops and random contrast, luminosity perturbations additionally to the obvious flips and noise addition.  </p>

<p>Look at the CIFAR-10 tutorial on TF website it is a good start. Plus TF now has <code>random_crop_and_resize()</code> which is quite useful.</p>

<p><strong>EDIT:</strong> The papers I am referencing <a href="https://arxiv.org/pdf/1503.07077v1.pdf" rel="nofollow">here</a> and <a href="https://arxiv.org/pdf/1502.05700v2.pdf" rel="nofollow">there</a>.</p>
image augmentation algorithms for preparing deep learning training set
<p>To prepare large amounts of data sets for training deep learning-based image classification models, we usually have to rely on image augmentation methods. I would like to know what are the usual image augmentation algorithms, are there any considerations when choosing them? </p>