"The ImageNet Classification Challenge, as it is called, involves training software on a collection of 1.5 million labeled images in 1,000 different categories, and then asking that software to use what it learned to label 100,000 images it has not seen before."
"Wu said that Minwa had made it possible to train the system on higher-resolution images. It also permitted use of a technique that turned the original 1.2 million training images into two billion by distorting them, flipping them, and altering their colors. Using that larger training set improved accuracy by preventing the system from becoming too fixated on the exact details of the training images, said Wu. The resulting system should be better at handling real-world photos, he said."
And they sort-of cheated to do it. I am sure if Google and MS would do a similar trick with their systems their accuracy would improve too.