Summary (long): pattern recognition comparisons

[Nici Schraudolph]

> From: Leonard Uhr <uhr at cs.wisc.edu>
> 
> Neural nets using backprop have only handled VERY SIMPLE images, usually in
> 8-by-8 arrays.  (We've used 32-by-32 arrays to investigate generation in
> logarithmically converging nets, but I don't know of any nets with complete
> connectivity from one layer to the next that are that big.) In sharp contrast,
> pr/computer vision systems are designed to handle MUCH MORE COMPLEX images (eg
> houses, furniture) in 128-by-128 or even larger inputs.  So I've been really
> surprised to read statements to the effect NN have proved to be much better.
> What experimental evidence is there that NN recognize images as complex as
> those handled by computer vision and pattern recognition approaches?

Well, Gary Cottrell for instance has successfully used a standard (3-layer,
fully interconnected) backprop net for various face recognition tasks from
64x64 images.  While I agree with you that many NN architectures don't scale
well to large input sizes, and that modular, heterogenous architectures have
the potential to overcome this limitation, I don't understand why you insist
that current NNs could only handle simple images - unless you consider any
image with less than 16k pixels simple.  Does face recognition qualify as a
complex visual task with you?

The whole point of using comparatively inefficient NN setups (such as fully
interconnected backprop nets) is that they are general enough to solve
complex problems without built-in heuristics.  Modular NNs require either
a lot of prior knowledge about the problem you are trying to solve, or a
second adaptive system (such as a GA) to search the architecture space.
In the former case the problem is comparatively easy, and in the latter
computational complexity rears its ugly head again... having said that,
I do believe that GA/NN hybrids will play an important role in the future.

I'm afraid I don't have a reference for Gary Cottrell's work - maybe
someone else can post the details?
--
Nici Schraudolph, C-014                nschraudolph at ucsd.edu
University of California, San Diego    nschraudolph at ucsd.bitnet
La Jolla, CA 92093                     ...!ucsd!nschraudolph