Suddenly, a leopard print sofa appears

I would propose that for this leopard problem, instead of just skewing the images, you also performed transformations on the COLOR and put the images back into the training set.

Maybe applying certain filters, such asdimming the saturation or contrast of images, so that the contrast between the leopoard spots were less visible (i.e. "A Leopoard in low lighting") - maybe this would force the neural net to learn more than just its print.

Knowing the right set of color filters to apply to all images could be tricky though.

> When Vladimir Vapnik teaches his computers to recognize handwriting, he does something similar. While there’s no whispering involved, Vapnik does harness the power of “privileged information.” Passed from student to teacher, parent to child, or colleague to colleague, privileged information encodes knowledge derived from experience. That is what Vapnik was after when he asked Natalia Pavlovich, a professor of Russian poetry, to write poems describing the numbers 5 and 8, for consumption by his learning algorithms. The result sounded like nothing any programmer would write. One of her poems on the number 5 read,

> He is running. He is flying. He is looking ahead. He is swift. He is throwing a spear ahead. He is dangerous. It is slanted to the right. Good snaked-ness. The snake is attacking. It is going to jump and bite. It is free and absolutely open to anything. It shows itself, no kidding. Brown_Cornerart

> All told, Pavlovich wrote 100 such poems, each on a different example of a handwritten 5 or 8, as shown in the figure to the right. Some had excellent penmanship, others were squiggles. One 5 was, “a regular nice creature. Strong, optimistic and good,” while another seemed “ready to rush forward and attack somebody.” Pavlovich then graded each of the 5s and 8s on 21 different attributes derived from her poems. For example, one handwritten example could have an ‘‘aggressiveness” rating of 2 out of 2, while another could show “stability” to a strength of 2 out of 3.

> So instructed, Vapnik’s computer was able to recognize handwritten numbers with far less training than is conventionally required. A learning process that might have required 100,000 samples might now require only 300. The speedup was also independent of the style of the poetry used. When Pavlovich wrote a second set of poems based on Ying-Yang opposites, it worked about equally well. Vapnik is not even certain the teacher has to be right—though consistency seems to count.

http://nautil.us/issue/6/secret-codes/teaching-me-softly

That article in turn reminded me strongly of "Metaphors We Live By" by Lakoff & Johnson, and the works they have written since, where they claim that humans make sense of the world using systems of rich, conceptual metaphors. As I understand, the work is well-known to machine learning researchers.