metric learning

Contrastive learning is how modern embeddings (CLIP, sentence encoders, SimCLR) are actually trained. What matters is the pull-positives-push-negatives objective, the InfoNCE loss, and why the number and hardness of negatives makes or breaks quality.

Metric learning succeeds or fails on the pairs you feed it. Random negatives teach almost nothing, and the margin plus the mining strategy determine whether the embeddings are any good. Here is how the choices interact.