the triplet loss architecture is unsupervised, is this correct?

[identidata]

hi, I am sorry this question may be more conceptual than technical.

When you use the triplet loss architecture, there are no labels. Is this correct?

You just compute the distance between the anchor subset of the batch and the positive subset of the batch, and the distance between the anchor subset of the batch and the negative subset of the batch; and then the resulting loss.

Of course, the underlying very strong premise is that within each batch, the first third is composed of anchor images, the second third of positive images, and the third third of negative images.

Is this correct?

One more question if I may.

In deploy mode, the batch size can be 1 right? Backward propagation through training makes sure that in deploy mode the weights will create a single embedding (batch size 1) from "fc9_1" which is close to embeddings of pictures belonging to the same person.

Is this correct?

Thanks so much.