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Convolutional Neural Nets versus kernel-based morphology transformations. What is better?

asked 2018-04-12 00:52:50 -0500

updated 2018-04-26 12:39:48 -0500

If somebody has experience or just tried to compare for himself, can you list the differences? Typical applications, limitations, different output ...

Programmatically, they are the same so I wonder, are there any differences besides the names used?

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please add some context.

berak gravatar imageberak ( 2018-04-12 02:31:46 -0500 )edit

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answered 2018-04-26 12:40:46 -0500

2 main differences may be outlined. 1.NN can work in 2 modes: learning and production. If convolution is used in learning, this is a completely different story. 2.As to production mode, kernel-based transformations are more general. You can write a sophisticated processing hardly imaginable with NN, but the nets are more natural. If you restrict the solution to this class, probably it will work like human vision.

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I never heared of production, but do you mean inference?

StevenPuttemans gravatar imageStevenPuttemans ( 2018-04-27 06:17:28 -0500 )edit

No. During learning you prepare the net, that is write something into its associative memory. Then, you stop training and make use of your product. Production system is a standard term in common symbolic programming. They are used for such tasks as parsing. Neural net in fact does something similar, only instead of rules it uses learned associations.

ya_ocv_user gravatar imageya_ocv_user ( 2018-05-02 05:59:52 -0500 )edit

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Asked: 2018-04-12 00:52:50 -0500

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Last updated: Apr 26 '18