[FFmpeg-devel] [FFmpeg-cvslog] Adds ESPCN super resolution filter merged with SRCNN filter.

Jean-Baptiste Kempf jb at videolan.org
Wed Jul 4 19:52:15 EEST 2018


Hello,

On Wed, 4 Jul 2018, at 16:19, Pedro Arthur wrote:
> 2018-07-04 4:03 GMT-03:00 Jean-Baptiste Kempf <jb at videolan.org>:
> > On Wed, 4 Jul 2018, at 01:22, Carl Eugen Hoyos wrote:
> >> 2018-07-04 0:14 GMT+02:00, Jean-Baptiste Kempf <jb at videolan.org>:
> >> > On Tue, 3 Jul 2018, at 20:59, Carl Eugen Hoyos wrote:
> >> >> How is this case different from many arrays in libavcodec/*data*?
> >> >
> >> > It is very different: the arrays in *data* come either from a
> >> > mathematical computation or a spec.
> >>
> >> (Apart from: Free and open specs?)
> >> This is probably true for some of the arrays, I think it is
> >> very unlikely that it's true for all of them.
> >
> > The point is: you can recreate all of those arrays.
> > If OP dies, you can still take over.
> >
> >> > Else, as some Debian Developer said: "It looks like code
> >> > hidden in an unsigned char array"
> >>
> >> Is it "code" or data that was computed with a copyrighted
> >> algorithm?
> It is only data, namely the weights used in the filters.

Sure, but how can we recreate this data, if you are not around?
How can we check your findings?

> > How can you know, if it is not explained, and you cannot reproduce it?
> > How is it different from a binary blob?
> >
> > Anything related to NN is very annoying, if you don't share the dataset and the methodology.
> 
> The paper cited in the code contains all relevant information for
> anyone with basic CNN knowledge to understand the model and maintain
> the code.
> The whole point of training a NN is that you do it once, takes a lot
> of time, and never do it again.
> If you look at the publications in this area, the concept of
> reproducibility is not the usually expected "bit exact" ,as other
> mathematical fields.
> It is more like achieving similar results, given the model and dataset.

I completely understand, having done some of that myself.

> For anyone interested in the training process, Sergey provided the
> repo [1] with scripts for downloading the dataset and training.

That's great!
What is the licensing of this?

-- 
Jean-Baptiste Kempf -  President
+33 672 704 734


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