Hands-On Java Deep Learning for Computer Vision

Differentiating inputs with Siamese networks

Let's see how the similarity function is implemented through Siamese networks. The idea was first implemented at paper published by Taigman in 2014, DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Then we will see how Siamese networks learn by giving a slightly more formal definition.

First, we will continue to use convolution architectures with many convolution layers:

The fully connected layers within neurons, and the softmax for the prediction.

Let's fit the first image we want to compare, X¹:

And what we will do is, through a forward pass, grab the activation values ...