[dpandya]

It seems that the primary contribution of this technique is that it uses specific assumptions supported by neuroscience research in order to allow for composability of learning and better generalization. By introducing these specific assumptions (e.g. contours define objects), they are able to reduce the complexity that the model has to learn and thereby reduce the amount of data that it needs.

Obviously, the question then becomes: what happens when you have visual situations that violate or come close to violating the assumptions made?

I'm not familiar enough with the specifics of RCNs to be able to answer this; maybe someone else can. Very interesting paper and approach regardless.

[joe_the_user]

After six or seven click-throughs, I downloaded the PDF.

I haven't read it but skimming, I could see that there definitely were no formulas in it at all . Which sort of says, at best what it tells you is "we did this thing, which is kind of like X and kind of like Y with Z changes". Essentially, no way to reproduce or understand by itself. The first reference then had a link behind a paywall...

So despite lots of apparent explanation, it seems like what they're actually doing is essentially unspecified (at least to the interested layman). It seems like at best an expert in the field of "compositional models" could say what is happening.

Also, the paper is published under the heading of an AI firm Fremont, ca rather than folks in a university, with the many authors listed by initial and last name...

PDF for the curious:

http://science.sciencemag.org/content/sci/early/2017/10/26/s...

Edit: tracked down that apparently has some "real" math. Whether is even what the OP is doing remains to be seen.

https://staff.fnwi.uva.nl/t.e.j.mensink/zsl2016/zslpubs/lake...

[boltzmannbrain]

70 page supplementary material: http://science.sciencemag.org/content/sci/suppl/2017/10/25/s...

Reference code: https://github.com/vicariousinc/science_rcn

[mannigfaltig]

I still find it incredibly hard to tell whether this is overblown hype or legit scientific progress. There is no indication whatsoever that this approach scales to deep feature hierarchies and that is likely what you need to compete on hard tasks like classification on ImageNet. Given the amount of money at play (several hundred millions of dollars), writing 70 pages, making code publishable is certainly an obvious way to get the most out of the hype.

[plaidfuji]

Haha yeah Science papers are about providing a high level explanation of what you did, in real words. Then you hit em with the 100 page supplemental that's got more detail than 3 papers' worth of research in other journals