Re:Computer Vision Research: The deep “depression”
by argonaut on Thursday June 09, 2016 @01:33PM
(#11870898)
No, we don't know why. You also have little basis to claim the biological visual cortex is optimal. It certainly works, which is enough for people who draw inspiration from it.
Re:Computer Vision Research: The deep “depression”
by trhway on Thursday June 09, 2016 @02:09PM
(#11871176)
that is my point - we have very good suspicion and experimental data (successful deep learning CNN as well as visual cortex) that it is optimal, at least in some very wide class if not global, and that warrants investigation for a proof of it. Proven optimality would be very telling, especially for biological visual cortex. Even if optimality doesn't happen for CNN Gabor, there may happen to be discovered reasons for why not, and thus that would help to construct even better, probably optimal, approach.
Re:Computer Vision Research: The deep “depression”
by argonaut on Friday June 10, 2016 @05:05AM
(#11875144)
Suspicion and empirical evidence cannot prove something is optimal. You cannot exhaustively empirically search an infinite space of models. You are seriously misunderstanding the definition of "optimal."
and do we know why? Usually it would mean some optimality. It should be relatively simple math here (back at the time at our University it would be given to a student as a thesis project and couple months later we'd have it), and that would give us 2 things - insight into biological visual cortex (which we suppose follows some optimality too and know we would have a very good candidate for the one) as well as to allow to start some primary convolutional layers with the (optimal set of) Gabors instead of going through learning them. Actually some of the best results i saw 15-20 years ago were produced by the simulation of visual cortex through such construction. And now image trained deep learning nets converge to the same.