The relevant embeddings here are computed by a neural network on high-dimensional input, like an image or a string of text. For instance, https://openai.com/research/clip. So they are not learned by backpropagating to the embedding itself (which is a common approach when you have a relatively small, fixed number of possible inputs, so you can just put your embeddings in a table). The embedding network will of course be trained by backpropagation, using some form of self-supervised learning
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