Traditionally, Siamese Neural Networks have two inputs. With some tweaking, you can get them to accept any number of inputs. What I don't understand is how to get them to accept variable numbers of inputs. I've seen a couple of research papers (most notably this one) where they talk about doing this, but none explain exactly how.

Could someone please explain how to create a Siamese Neural Network with a variable number of inputs?

  • $\begingroup$ Hello. Welcome to Artificial Intelligence Stack Exchange. Can you please provide the links to the papers that you read so far (that you don't understand)? $\endgroup$
    – nbro
    Dec 7 '21 at 17:34
  • $\begingroup$ I've added the main one now, thanks. $\endgroup$ Dec 7 '21 at 22:00
  • $\begingroup$ After more research, I've found that magnet loss could present a solution. I would say more, but I can't find an implementation in keras (the framework I use) and my question about it is still unanswered. I'll make sure to edit this if I get a reply. I would love to combine the research done in both the magnet loss paper and the VIS-CNN paper, but I currently don't have the skill to $\endgroup$ Dec 7 '21 at 22:07

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