Suddenly, a leopard print sofa appears
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Suddenly, a leopard print sofa appears
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Re: Suddenly, a leopard print sofa appears
#2For context, ImageNet does have a sofa category for labels :/
Re: Suddenly, a leopard print sofa appears
#3Somehow tangent but this made me think about this quote found on HN last year:
Context: Evolutionary algorithms and analog electronic circuits
> One thing stands out when you try playing with evolutionary systems. Evolution is _really_ good at gaming the system. Unless you are very careful at specifying all of the constraints that you care about you can end up with a solution that is very clever but not quite what you had in mind. Here power consumption is the issue. If you tried to evolve a sturdy chair you might end up with something that is 1mm tall. or maybe a fuel efficient car that exploits continental drift.
I think it's the same here: The net is never gonna better than what it needs to be, and it is probably always gonna take the easy route.
Re: Suddenly, a leopard print sofa appears
#4I'm happy somebody tries to put some sense into the whole absurdly overblown machine learning field.
Re: Suddenly, a leopard print sofa appears
#5[deleted]
Re: Suddenly, a leopard print sofa appears
#6I'm happy somebody tries to put some sense into the whole absurdly overblown machine learning field.
From the outside, there appear to be significant advances; it's just they seem to come in clusters, rather than linearly. The news has turned into science fiction!
It would seem CNNs were a significant step up, but the author hints at inferring structure as the next tack to take.
Re: Suddenly, a leopard print sofa appears
#7I'm happy somebody tries to put some sense into the whole absurdly overblown machine learning field.
From the outside, there appear to be significant advances; it's just they seem to come in clusters, rather than linearly. The news has turned into science fiction!
It would seem CNNs were a significant step up, but the author hints at inferring structure as the next tack to take.
Re: Suddenly, a leopard print sofa appears
#8This is fascinating and well written.
I tried the unrotated sofa image on Wolfram's ImageIdentify and it correctly identified a settee [1]. So it presumably gathered that from the shape of the image rather than the pattern. It is peculiar though that it can't see the shape under a simple rotation. Or perhaps the margin of confidence levels between sofa and leopard were so narrow that a rotation was enough to tip it in favour of the leopard? I'd be interested to see the inner workings of this.
Re: Suddenly, a leopard print sofa appears
#9That's right, CNNs tend to be more concerned with textures rather than with overall shapes. One problem is that CNNs discard a lot of pose information of the detected features during pooling. Another problem is that there is no top-down verification such as "hmm, leopards always have {heads, legs, tails ...}, I should scan the input for these... nope, it doesn't fit at all, I should exclude everything that has {heads, legs, tails ...} from my interpretation." In a human brain that likely happens in some distributed fashion without considering individual classes, but by just inhibiting everything that can't be verified upon looking twice (or more times).
Re: Suddenly, a leopard print sofa appears
#10> So I guess, there's still a lot of work to be done.
And I think this is the most interesting part.
One of the most depressing things about all of the "this image recognition algorithm performs better than humans on this task" is the idea that we've pretty much solved the problem, and it's just a matter of some more optimization and tweaking to handle a few edge cases.
This kind of problem, where the dominant solution simply gets it so wrong, and the problem cases are uncommon enough that any statistical solution is generally going to treat them as noise, reveals that in fact that there is likely plenty of room for entirely new, novel ways of approaching the problem to handle these kinds of cases better.
It's actually more exciting that there's so much more to be done, than to say "well, it's basically a solved problem, we just need to do some tweaking and optimization."