The tricky bit with this task is context. For example, my 'spider' sucked big time, and I started trying to draw a web right at the end (before running out of time). This is exactly how I would play 'Pictionary', with plenty of big arrows pointing to the part of the drawing representing the word. However, that really isn't appropriate when trying to train a neural net to recognise abstract images.
Similar complexities arise in relation to perspective (should 'bridge' be side-on or top-down?) and even something as obvious as homonyms (I drew 'nail' as the small metal thing you use to hang a picture, but I'm sure others would have drawn the thing at the end of a finger).
I guess my point is that this comes across very much as a game (maybe that's all it really is), which will probably give poor 'neural net training' results, as opposed to if it weren't a game - e.g. by increasingly the ridiculously short time limit. I'm sure the results will still be interesting, but I think they'll end up very abstract rather than very accurate - maybe we'll just 'invent' a new set of logograms.
Similar complexities arise in relation to perspective (should 'bridge' be side-on or top-down?) and even something as obvious as homonyms (I drew 'nail' as the small metal thing you use to hang a picture, but I'm sure others would have drawn the thing at the end of a finger).
I guess my point is that this comes across very much as a game (maybe that's all it really is), which will probably give poor 'neural net training' results, as opposed to if it weren't a game - e.g. by increasingly the ridiculously short time limit. I'm sure the results will still be interesting, but I think they'll end up very abstract rather than very accurate - maybe we'll just 'invent' a new set of logograms.