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Joel_Duncan t1_jczkbc0 wrote

I keep seeing this getting asked like people are expecting a magic bullet solution.

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In general you can only get out something within the realm of what you put in.

There are intelligent ways to structure training and models, but you can't fill in expected gaps without training with a reference or a close approximation of what those gaps are.

My best suggestion is to limit your input data or muxed model to specific high resolution subsets.

ex. You can train a LoRa on a small focused subset of data.

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i_sanitize_my_hands OP t1_jczyrsl wrote

Not expecting a magic bullet solution. Been in the field long enough to know that.

However, any written record of the intelligent ways you mentioned are valuable and worth going through.

One of the reasons it gets asked a lot is because image quality analysis doesn't seem to get enough air time. There are only few papers and sone as old as 2016. They font reflect the trend since 'all you need is attention '

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