The network guessed some of the "correct" answers far to quickly. For example, with just an L shape (|_) it guessed suitcase. Feels like the model is suffering from overfitting.
Fun though.
Fun though.
I was asked to draw 'eyeglasses', kept guessing 'glasses'.
I guess it would have been more of an A.I. challenge if the premise was; draw anything and I'll try to guess what it is.
It could figure out most of my drawings but it would get them well in advance of me completing anything substantial (like others, I would be asked to draw a leg and draw just a curved line and it would guess leg before I finished).
Trying to draw what it asked for but with some unusual features (like lines or dot patterns around what it asked for before drawing it) and it gets extremely confused; it doesn't really seem to be good at filtering out any noise: http://imgur.com/a/oE1j2 (gallery of results and what it thought it saw.
Drawing things it didn't ask for just to see what it was guessing resulted in some really strange responses and fits. The answer set it has is extremely limited, so something like a hand giving the horns (\m/) was last guess a duck. A moose was a scorpion, then a duck, then a hand. Godzilla (or a bipedal dinosaur if you prefer) was a vase, then a scorpion, then a boat. My loaf of bread was a washing machine, an anvil, then a postcard. The Deathstar was a bandage, a helicopter, and a lighthouse. And a chainsaw was considered an aircraft.
Between the disruptive patterns and drawing things outside of it's vocabulary, the system seems really confused. Looking at the comparison results, I can see how when drawing some things it got it real fast. (Tennis Rackets were mostly defined by a crosshatch pattern, Harps by a series of parallel vertical lines). This makes sense. For other things, not as much.
It might be a more convincing presentation to give the user a list of items the machine knows (the full list) and tell the user to try to draw some, and then the computer could check it off as it gets them. That seems like a better way of presenting this than "Draw a box. hey! you drew a box! Isn't that cool?"
To me this is another interesting distinction on the NN recognition versus a human recognition - QuickDraw having a limited "vocabulary" to refer to really highlights this, as does my own lack of knowledge of Roobarb. Some of these things can really blindside us, and I suspect that it's going to require a lot of human hand-holding for awhile for the machines to get a strong vocabulary.
For some time I've been pondering how far you could take a machine's tabula rasa learning, for something like language, and how closely it would mimic a child's learning. (Language, color, math, etc).
Also, I realized how incredibly hard I suck at drawing.
This. I drew an Ant but the system couldn't guess it. Later it showed me how normal people draw ants.
Man, i suck at drawing ants
It should give you 8 ~10 words to choose from.
It also seems to constantly have a "best guess" to some degree and if that happens to be correct it confirms pretty quickly.
EDIT: Drew a "cake", it guessed "birthday cake". Wrong answer apparently.
So if you draw a simple shape it start to go trough the list of things he recognize and end the game there.
It works great for this game because he can have very fast answer but only work win the cases when you actually have a feedback that eliminate all the wrong guesses.
Basically if it simply went trought the whole english dictionary fast enough he could get the same result without even looking at the pictures.