Re: Binary descriptors for OpenCV

[Saku Laesvuori <saku@laesvuori.fi>]

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> >You can always check what kind of data the program gives to the
> >neural network as the program is free software. If the data is valid
> >runtime input it is also valid training data.
> 
> That's not necessarily true. Like an image generating program will be
> trained on image + caption pairs, but running it involves giving it
> just the captions. Thus, running the model doesn't inherently show you
> how to retrain the model.

In that case aren't the captions the input to the model and the images
the output? The training process tries to minimize the error between the
correct output from the data set and the generated output. From using
the model you do know what formats are expected as input and output, so
you do have the information required for retraining.

> >You can't exactly *know* that any extra training doesn't break the
> >model but the same holds for editing the original training data.
> 
> You can know with more certainty that it doesn't break the model.

Well, that depends on what kind of data editing and extra training we
are comparing. If we remove a tiny bit from the original data set, it
obviously is less likely to break the model than retraining it with bad
data. But if you had a new training data set, it would not be any
different to retrain the pretrained model on it instead of adding the
new data set into the original training data and training from scratch.

I should probably mention that I have never tried retraining models in
practice and I'm basing all my arguments on my theoretical understading
of how machine learning models work.

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