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To handle discrete metadata with high cardinality (lots of categories), FINO uses a momentum-updated prototype bank for discrete factors. The loss used is a contrastive loss, inspired by supervised contrastive learning. For continuous metadata, the loss just regresses a small predictor head against the metadata target. Of course, no metadata is needed at inference, it is only used to guide learning.
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