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I’d be surprised if it’d be useful for something like cars or soccer players, or really anything that may not have a continuous mapping. I guess more generally

I’d be surprised if it’d be useful for something like cars or soccer players, or really anything that may not have a continuous mapping. I guess more generally whenever the underlying “true” similarity function is not differentiable — categorical data springs to mind (cars, football players…). I could see it making sense for complex unstructured data — Qdrant seems to point in that direction.

monatis 4y ago
Some loss functions such as ArcFace loss and CosFace loss enforce the encoder model to organize their latent space in such a way that categories are placed with an angular margin from one another. Thus the model implicitly learns a continuous distance function. Fun fact, one of the examples in Quaterion is for similar cars search. If you find this topic and want to discover more, we collected a bunch of resources that might be helpful. https://github.com/qdrant/awesome-metric-learning https://github.com/qdrant/awesome-metric-learning