Re: Binary descriptors for OpenCV

[Nathan Dehnel]

>If you know how to convert the blob to weights in the neural network
(something the program has to do to make any use of the blob) and know
the error function, you can continue the training with new data.

Yeah, I get that, but you don't necessarily know what the weights
mean. Let's charitably assume you know the blob works on image data
(instead of audio data or whatever). Do you know if it needs to be
trained on images of a particular size, or color depth, or encoding,
or color format, etc.? And what about models for more complex data
than images like genetic data? How do you know you're not going to end
up with a network that spews out invalid garbage if you re-train it
with things that are incompatible with the original training dataset?
And how do you know that, beyond trial and error, unless you have the
original dataset?

On Tue, Aug 1, 2023 at 3:37 PM Saku Laesvuori <saku@laesvuori.fi> wrote:
>
> > Is this even practically possible? How do you re-train a blob you know
> > nothing about? To me this sounds similar to saying a compiled binary
> > is free software if the license allows you to decompile it and
> > deobfuscate it.
>
> If you know how to convert the blob to weights in the neural network
> (something the program has to do to make any use of the blob) and know
> the error function, you can continue the training with new data.
>
> This is not any different from training the model from scratch. In both
> cases we begin with some set of initial weights for a huge polynomial,
> take a sample of our training data, compute the polynomial for it and
> tweak the weights a bit if the result was not what we wanted. The only
> difference is that when training from scratch we begin with very bad
> guesses for all the weights. When we are tuning the blob we begin with
> much better guesses that are closer to the values we would actually
> want.
>
> The difference to a compiled binary program is that you would want to
> edit it in the source code form. You really would not want to edit the
> neural network by editing the original training data and retraining the
> entire network from scratch. The data set probably contains thousands,
> tens of thousands or even more random pictures that you would have to go
> through and see if they represent the data and results you want. It
> would be much easier to test whether the network gives the correct
> results and train it with new data that you know describes your problem
> better.