I have to find the closest match between my image and a bunch of already collected images of different classes in the folder.

Which of the meta-learning approach should I select?

I am thinking about the Siamese or matching network. In Siamese, I have to match my image during testing with all existing images in the folder to find the correct match.

So, do you think that I can use a matching network and produce a better result? What is the parameter based on which developer decides where to use matching/prototypical network and where to use Siamese network?

Note: at testing time, new unseen images classes can be added on which model is not trained. Do you think a matching network will still work for this case?


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