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There are known architectures to implement this idea, namely, seamese networks, and also a training strategy, known as contrastive learning, that relies on the idea of comparing the output of neural networks. I will explain both of them briefly. The idea of seamese networks is exactly what you mentioned. You have a single model, $m$, that receives two inputs,...

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