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Figure 1
Triplet network architecture for model training. Three input examples (anchor, positive and negative) are propagated through the triplet NN simultaneously. Anchor and positive share the same label, thus forming a matching pair. In contrast, anchor and negative do not share the same label, thus forming an opposing pair. The three CNNs and FC layers share the same weights in the triplet network. After examples are embedded to a low-dimensional vector space, a triplet loss function is used to simultaneously maximize similarities between matching embeddings and minimize those between opposing embeddings. A side-by-side comparison of three embeddings in a triplet are annotated at the upper right corner.

IUCrJ
Volume 10| Part 5| September 2023| Pages 568-578
ISSN: 2052-2525
https://doi.org/10.1107/S2052252523006115