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Well, I suppose one can use some kind of contrastive learning in this case. A famous example of the establishment of relation between two different representations is the CLIP - Contrastive Language–Image Pre-training, where model gets a huge corpus of image captions and images and the image caption is passed through the language model, and the image itself ...


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In general term yes. Because what the ML algorithms do in general is to learn the hidden probability density function of the target examples (cats, dogs..). And that is done by learning the conditional probability function between inputs, $X$, and target outputs, $y$, for discriminative models or by learning the joint probability function for generative ...


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