4 ms·
Let's not forget that the word "imperceptible" is a heavily laden term in this context. There are numerous modifications to the data that would be "imperceptibl
by iandanforth 12y ago
Let's not forget that the word "imperceptible" is a heavily laden term in this context. There are numerous modifications to the data that would be "imperceptible" to a machine learning system, but would completely confuse a human. For example if you were to divide the image into a grid, and shuffle the squares, many ML systems would be tolerant to this kind of modification because some training regimes do this anyway. To that system you haven't changed anything important about the image and it would correctly classify it.
What this result says to me is that there are really useful features of the data that can successfully classify images that humans are totally unaware of! And that's neat.
- segmondy 12y agoI disagree with your opinion. What this says to me is that DNN is not how humans classifies images.
- maaku 12y agoWhich would be defeating a strawman. I don't know anyone who claims DNN is precisely how any aspect of the human brain works.
- galvan 12y agoIn the linked paper [0], they actually tested that point: after applying Gaussian noise to samples, the model could still recognize them half the time[1], despite being nearly unreadable (to me). [0] http://cs.nyu.edu/~zaremba/docs/understanding.pdf http://cs.nyu.edu/~zaremba/docs/understanding.pdf [1] http://puu.sh/9B4eG/1e9f7eb56b.png http://puu.sh/9B4eG/1e9f7eb56b.png