In the paper Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012., section 4.1, the authors describe their data augmentation process. They say they increased the size of the training set by a factor of 2048. Does this mean they trained on a total of 2048 × 1.2 millions images?
Moreover, I don't quite follow this:
At test time, the network makes a prediction by extracting ﬁve 224 × 224 patches (the four corner patches and the center patch) as well as their horizontal reﬂections (hence ten patches in all), and averaging the predictions made by the network’s softmax layer on the ten patches.
What do they mean they extracted ﬁve 224 × 224 patches (corner, center and horizontal)? And why does it result in ten patches in total?