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I'm training a model where the samples success rate is low.

I mean how do I tackle such situation - maybe only show the samples which match but then the ones that doesn't may match too.

But on the other hand if I train it with all the samples it seems like it never fully matches when it should.

Any ideas?

AnArrayOfFunctions
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  • I think you need to specify your question: What do you mean by 'samples success rate', 'samples that match' and what is 'it' referring to in 'seems like it never fully matches when it should'?. – Chillston Sep 18 '22 at 13:48
  • Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. – Community Sep 18 '22 at 17:52
  • Are you referring to something related to class imbalance? – Dave Sep 18 '22 at 19:54

1 Answers1

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You have a situation where class imbalance is quite high. There are two ways to tackle this:

  1. Use Focal loss which focuses on underrepresented classes.
  2. Train a Siamese network and allied techniques to it.
Abhishek Verma
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