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Uploading a URL of white noise from Wikipedia gave it a high-memorability (0.82) with lots of areas of interest. Secondly uploading a purely white image [2] p

by stkni 11y ago

Uploading a URL of white noise from Wikipedia gave it a high-memorability (0.82) with lots of areas of interest. Secondly uploading a purely white image [2] produced high areas of interest in the top corners and a mediumly interesting image (0.62). I thought the tests might reveal something useful, like the eye-tracking heat-maps of Jakob Nielsen [3] but I'm not convinced. [1] https://upload.wikimedia.org/wikipedia/commons/f/f6/White-noise-mv255-240x180.png https://upload.wikimedia.org/wikipedia/commons/f/f6/White-no... [2] http://images.all-free-download.com/images/graphiclarge/plain_white_background_211387.jpg http://images.all-free-download.com/images/graphiclarge/plai... [3] https://www.nngroup.com/books/eyetracking-web-usability/ https://www.nngroup.com/books/eyetracking-web-usability/

svantana 11y ago
A machine-learned system is only as good as its training data, at best. In this case, it was trained on natural-looking images, so results on "unnatural" images will be unpredictable/random/wrong. One way to fix this would be to provide "bug bounty"-style rewards for producing images that makes the system deviate significantly from mechanical turk workers performing the same task. I wouldn't be surprised to see google/fb etc starting such programs in the near future, as their ML systems reach maturity.