Asking out of curiosity, because I have limited experience in that domain.
I thought that fine-tuning was changing the weights in the model, not the embedding? Or did I misunderstand?
I thought that fine-tuning was changing the weights in the model, not the embedding? Or did I misunderstand?
For example you might have one model which embeds a text query and another model which embeds an image. You also have a dataset of image + text captions. Training means updating the weights of those models so that the embedding of the image is close to the embedding of its corresponding caption.