TLDR: The point is to correct a known flaw with image recognition algorithms. I'm not a professional researcher, but here's what I gather from reading other ar

TLDR: The point is to correct a known flaw with image recognition algorithms. I'm not a professional researcher, but here's what I gather from reading other articles: One major problem with image recognition in machines is that while they are generally able to recognize real images correctly, they are 1) Easily fooled 2) Unable to have human-type image understanding. For example, you can recognize an animated character as a human, even though you may never have seen that particular style of drawing before. One major problem that people realized is that deep neural networks have a tendency to recognize individual facets, for example, a nose. So if you want a neural network to believe something is a human, pepper it with as many noses as you can. It knows that humans have noses so the more noses the more human it must be. Of course, if we saw an image like this we wouldn't think that it was a human because we know a human has only one nose. To me it seems the primary thrust of this research is to generate a NN that can recognize, like a human, that if an entity has 10 eyes, it's probably not a cat. This is alluded to with the house of horrors cat photographs at the beginning of the article. You can see that when passed an image of a cat with 3 eyes, this neural network correctly removed one of the eyes to make it more realistic. Here is an article explaining this problem: https://arxiv.org/pdf/1412.1897.pdf https://arxiv.org/pdf/1412.1897.pdf