een two or more sister networks,
each producing embedding vectors of its respective inputs. In supervised similarity learning, the networks are then trained to maximize the contrast (distance) between embeddings of inputs of different classes,
while minimizing the distance between embeddings of similar classes, resulting in embedding spaces that reflect the class segmentation of the training inputs. <a class="reference external" href="https://keras.io/examples/vision/siamese_contrastive/">[1]</a></p>
</div>